{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function,division,absolute_import\n",
    "import tables\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import to_categorical\n",
    "from keras.models import model_from_json\n",
    "from custom_layers import Conv1D_linearphase, DCT1D\n",
    "from heartnet_v1 import reshape_folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_name = 'fold1+compare 2018-04-17 22:03:55.445492'\n",
    "checkpoint_name = \"/media/taufiq/Data1/heart_sound/models/fold1+compare 2018-05-05 17:04:36.995687/weights.0007-0.8148.hdf5\"\n",
    "min_epoch = 100\n",
    "min_metric = .7\n",
    "confidence_thresh = 0.2\n",
    "\n",
    "foldname = 'fold1+compare'\n",
    "fold_dir = '/media/taufiq/Data1/heart_sound/feature/segmented_noFIR/'\n",
    "model_dir = '/media/taufiq/Data1/heart_sound/models/'\n",
    "log_dir = '/media/taufiq/Data1/heart_sound/logs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model from checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold1+compare 2018-05-05 17:04:36.995687\n"
     ]
    }
   ],
   "source": [
    "model_dir = checkpoint_name[:checkpoint_name.find('fold')]\n",
    "log_name = checkpoint_name[checkpoint_name.find('fold'):checkpoint_name.find('weights')-1]\n",
    "print(log_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model directory found\n",
      "model.json found. Importing\n"
     ]
    }
   ],
   "source": [
    "if os.path.isdir(model_dir+log_name):\n",
    "    print(\"Model directory found\")\n",
    "    if os.path.isfile(os.path.join(model_dir+log_name,\"model.json\")):\n",
    "        print(\"model.json found. Importing\")\n",
    "    else:\n",
    "        raise ImportError(\"model.json not found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(model_dir+log_name,\"model.json\")) as json_file:\n",
    "    loaded_model_json = json_file.read()\n",
    "model = model_from_json(loaded_model_json,{'Conv1D_linearphase':Conv1D_linearphase,'DCT1D':DCT1D})\n",
    "model.load_weights(checkpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 2500, 1)      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_linearphase_1 (Conv1D_li (None, 2500, 1)      31          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_linearphase_2 (Conv1D_li (None, 2500, 1)      31          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_linearphase_3 (Conv1D_li (None, 2500, 1)      31          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_linearphase_4 (Conv1D_li (None, 2500, 1)      31          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 2496, 8)      40          conv1d_linearphase_1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 2496, 8)      40          conv1d_linearphase_2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 2496, 8)      40          conv1d_linearphase_3[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 2496, 8)      40          conv1d_linearphase_4[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 2496, 8)      32          conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 2496, 8)      32          conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 2496, 8)      32          conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 2496, 8)      32          conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 2496, 8)      0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 2496, 8)      0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 2496, 8)      0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 2496, 8)      0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 2496, 8)      0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 2496, 8)      0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 2496, 8)      0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 2496, 8)      0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 1248, 8)      0           dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 1248, 8)      0           dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 1248, 8)      0           dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_7 (MaxPooling1D)  (None, 1248, 8)      0           dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 1244, 4)      160         max_pooling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 1244, 4)      160         max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 1244, 4)      160         max_pooling1d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 1244, 4)      160         max_pooling1d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 1244, 4)      16          conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 1244, 4)      16          conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 1244, 4)      16          conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 1244, 4)      16          conv1d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 1244, 4)      0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 1244, 4)      0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 1244, 4)      0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 1244, 4)      0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 1244, 4)      0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 1244, 4)      0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 1244, 4)      0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 1244, 4)      0           activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 622, 4)       0           dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 622, 4)       0           dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1D)  (None, 622, 4)       0           dropout_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_8 (MaxPooling1D)  (None, 622, 4)       0           dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 622, 16)      0           max_pooling1d_2[0][0]            \n",
      "                                                                 max_pooling1d_4[0][0]            \n",
      "                                                                 max_pooling1d_6[0][0]            \n",
      "                                                                 max_pooling1d_8[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dc_t1d_1 (DCT1D)                (None, 622, 16)      0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 9952)         0           dc_t1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 20)           199040      flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 20)           0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 2)            42          dropout_9[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 200,198\n",
      "Trainable params: 199,978\n",
      "Non-trainable params: 220\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3283\n",
      "515\n",
      "(93942, 2500, 1)\n",
      "(93942, 1)\n",
      "(15511, 2500, 1)\n",
      "(15511, 1)\n"
     ]
    }
   ],
   "source": [
    "############## Importing data ############\n",
    "feat = tables.open_file(fold_dir + foldname + '.mat')\n",
    "x_train = feat.root.trainX[:]\n",
    "y_train = feat.root.trainY[0, :]\n",
    "x_val = feat.root.valX[:]\n",
    "y_val = feat.root.valY[0, :]\n",
    "train_parts = feat.root.train_parts[0, :]\n",
    "val_parts = feat.root.val_parts[0, :]\n",
    "\n",
    "############## Relabeling ################\n",
    "\n",
    "for i in range(0, y_train.shape[0]):\n",
    "    if y_train[i] == -1:\n",
    "        y_train[i] = 0  ## Label 0 for normal 1 for abnormal\n",
    "for i in range(0, y_val.shape[0]):\n",
    "    if y_val[i] == -1:\n",
    "        y_val[i] = 0\n",
    "\n",
    "############# Parse Database names ########\n",
    "\n",
    "train_files = []\n",
    "for each in feat.root.train_files[:][0]:\n",
    "    train_files.append(chr(each))\n",
    "print(len(train_files))\n",
    "val_files = []\n",
    "for each in feat.root.val_files[:][0]:\n",
    "    val_files.append(chr(each))\n",
    "print(len(val_files))\n",
    "\n",
    "################### Reshaping ############\n",
    "\n",
    "x_train, y_train, x_val, y_val = reshape_folds(x_train, x_val, y_train, y_val)\n",
    "y_train = to_categorical(y_train, num_classes=2)\n",
    "y_val = to_categorical(y_val, num_classes=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93514, 2500, 1)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_idx = 0\n",
    "drop_idx = []\n",
    "for idx,s in enumerate(train_parts):\n",
    "    if idx in excl1:\n",
    "        drop_idx.append(np.r_[start_idx:start_idx + int(s)])\n",
    "    start_idx = start_idx + int(s)\n",
    "drop_idx = np.hstack(drop_idx)\n",
    "np.delete(x_train,drop_idx,axis=0).shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Training and Validation Filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filenames Loaded : Train files 3283 and Validation Files 515\n"
     ]
    }
   ],
   "source": [
    "import matlab.engine\n",
    "eng = matlab.engine.start_matlab()\n",
    "fold1_filenames = eng.load(os.path.join(fold_dir,'fold1_filenames.mat'))\n",
    "compare_filenames = eng.load(os.path.join(fold_dir,'compare_filenames.mat'))\n",
    "eng.quit()\n",
    "\n",
    "train_filenames = fold1_filenames['train_files']\n",
    "train_filenames.extend(compare_filenames['train_files'])\n",
    "val_filenames = fold1_filenames['val_files']\n",
    "val_filenames.extend(compare_filenames['val_files'])\n",
    "print(\"Filenames Loaded : Train files {} and Validation Files {}\".format(len(train_filenames),len(val_filenames)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filenames</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a0156.wav</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a0148.wav</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a0099.wav</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b0265.wav</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b0319.wav</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   filenames dataset\n",
       "0  a0156.wav       a\n",
       "1  a0148.wav       a\n",
       "2  a0099.wav       a\n",
       "3  b0265.wav       b\n",
       "4  b0319.wav       b"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTrain = pd.DataFrame(train_filenames,columns={\"filenames\"})\n",
    "dfVal = pd.DataFrame(val_filenames,columns={\"filenames\"})\n",
    "dfTrain['dataset'] = train_files\n",
    "dfVal['dataset'] = val_files\n",
    "dfVal.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get true labels per recording and append "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "true = []\n",
    "start_idx = 0\n",
    "y_val_cc = np.transpose(np.argmax(y_val, axis=-1))\n",
    "for s in val_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    # ~ print \"part {} start {} stop {}\".format(s,star_idx,start_idx+int(s)-1)\n",
    "    temp_ = y_val_cc[start_idx:start_idx + int(s) - 1]\n",
    "    if (sum(temp_ == 0) > sum(temp_ == 1)):\n",
    "        true.append(0)\n",
    "    else:\n",
    "        true.append(1)\n",
    "    start_idx = start_idx + int(s)\n",
    "dfVal['true'] = true\n",
    "\n",
    "true = []\n",
    "start_idx = 0\n",
    "y_train_cc = np.transpose(np.argmax(y_train, axis=-1))\n",
    "for s in train_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    # ~ print \"part {} start {} stop {}\".format(s,start_idx,start_idx+int(s)-1)\n",
    "    temp_ = y_train_cc[start_idx:start_idx + int(s) - 1]\n",
    "    if (sum(temp_ == 0) > sum(temp_ == 1)):\n",
    "        true.append(0)\n",
    "    else:\n",
    "        true.append(1)\n",
    "    start_idx = start_idx + int(s)\n",
    "dfTrain['true'] = true"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Append true labels and signal quality index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "true = []\n",
    "start_idx = 0\n",
    "y_val_cc = feat.root.valY[1,:]\n",
    "for s in val_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    # ~ print \"part {} start {} stop {}\".format(s,star_idx,start_idx+int(s)-1)\n",
    "    temp_ = y_val_cc[start_idx:start_idx + int(s) - 1]\n",
    "    if (sum(temp_ == 0) > sum(temp_ == 1)):\n",
    "        true.append(0)\n",
    "    else:\n",
    "        true.append(1)\n",
    "    start_idx = start_idx + int(s)\n",
    "dfVal['quality'] = true\n",
    "\n",
    "true = []\n",
    "start_idx = 0\n",
    "y_train_cc = feat.root.trainY[1,:]\n",
    "for s in train_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    # ~ print \"part {} start {} stop {}\".format(s,start_idx,start_idx+int(s)-1)\n",
    "    temp_ = y_train_cc[start_idx:start_idx + int(s) - 1]\n",
    "    if (sum(temp_ == 0) > sum(temp_ == 1)):\n",
    "        true.append(0)\n",
    "    else:\n",
    "        true.append(1)\n",
    "    start_idx = start_idx + int(s)\n",
    "dfTrain['quality'] = true\n",
    "\n",
    "true = []\n",
    "start_idx = 0\n",
    "y_val_cc = np.transpose(np.argmax(y_val, axis=-1))\n",
    "for s in val_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    # ~ print \"part {} start {} stop {}\".format(s,star_idx,start_idx+int(s)-1)\n",
    "    temp_ = y_val_cc[start_idx:start_idx + int(s) - 1]\n",
    "    if (sum(temp_ == 0) > sum(temp_ == 1)):\n",
    "        true.append(0)\n",
    "    else:\n",
    "        true.append(1)\n",
    "    start_idx = start_idx + int(s)\n",
    "dfVal['true'] = true\n",
    "\n",
    "true = []\n",
    "start_idx = 0\n",
    "y_train_cc = np.transpose(np.argmax(y_train, axis=-1))\n",
    "for s in train_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    # ~ print \"part {} start {} stop {}\".format(s,start_idx,start_idx+int(s)-1)\n",
    "    temp_ = y_train_cc[start_idx:start_idx + int(s) - 1]\n",
    "    if (sum(temp_ == 0) > sum(temp_ == 1)):\n",
    "        true.append(0)\n",
    "    else:\n",
    "        true.append(1)\n",
    "    start_idx = start_idx + int(s)\n",
    "dfTrain['true'] = true"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model from log name\n",
    "\n",
    "\n",
    "Given a log_name it scans through the directories and finds the best three weights considering Sensitivity, Specificity and Macc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model directory found\n",
      "model.json found. Importing\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 2500, 1)      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_linearphase_1 (Conv1D_li (None, 2500, 1)      31          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_linearphase_2 (Conv1D_li (None, 2500, 1)      31          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_linearphase_3 (Conv1D_li (None, 2500, 1)      31          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_linearphase_4 (Conv1D_li (None, 2500, 1)      31          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 2496, 8)      40          conv1d_linearphase_1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 2496, 8)      40          conv1d_linearphase_2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 2496, 8)      40          conv1d_linearphase_3[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 2496, 8)      40          conv1d_linearphase_4[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 2496, 8)      32          conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 2496, 8)      32          conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 2496, 8)      32          conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 2496, 8)      32          conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 2496, 8)      0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 2496, 8)      0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 2496, 8)      0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 2496, 8)      0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 2496, 8)      0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 2496, 8)      0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 2496, 8)      0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 2496, 8)      0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 1248, 8)      0           dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 1248, 8)      0           dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 1248, 8)      0           dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_7 (MaxPooling1D)  (None, 1248, 8)      0           dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 1244, 4)      160         max_pooling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 1244, 4)      160         max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 1244, 4)      160         max_pooling1d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 1244, 4)      160         max_pooling1d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 1244, 4)      16          conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 1244, 4)      16          conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 1244, 4)      16          conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 1244, 4)      16          conv1d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 1244, 4)      0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 1244, 4)      0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 1244, 4)      0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 1244, 4)      0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 1244, 4)      0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 1244, 4)      0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 1244, 4)      0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 1244, 4)      0           activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 622, 4)       0           dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 622, 4)       0           dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1D)  (None, 622, 4)       0           dropout_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_8 (MaxPooling1D)  (None, 622, 4)       0           dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 622, 16)      0           max_pooling1d_2[0][0]            \n",
      "                                                                 max_pooling1d_4[0][0]            \n",
      "                                                                 max_pooling1d_6[0][0]            \n",
      "                                                                 max_pooling1d_8[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 9952)         0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Dense (Dense)                   (None, 20)           199040      flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 20)           0           Dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Output (Dense)                  (None, 2)            42          dropout_9[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 200,198\n",
      "Trainable params: 199,178\n",
      "Non-trainable params: 1,020\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "if os.path.isdir(model_dir+log_name):\n",
    "    print(\"Model directory found\")\n",
    "    if os.path.isfile(os.path.join(model_dir+log_name,\"model.json\")):\n",
    "        print(\"model.json found. Importing\")\n",
    "    else:\n",
    "        raise ImportError(\"model.json not found\")\n",
    "\n",
    "with open(os.path.join(model_dir+log_name,\"model.json\")) as json_file:\n",
    "    loaded_model_json = json_file.read()\n",
    "model = model_from_json(loaded_model_json,{'Conv1D_linearphase':Conv1D_linearphase,'DCT1D':DCT1D})\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_csv = os.path.join(log_dir+log_name,\"training.csv\")\n",
    "df = pd.read_csv(training_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_idx = df['val_sensitivity'][df.epoch>min_epoch][df.val_specificity>min_metric].idxmax()\n",
    "spec_idx = df['val_specificity'][df.epoch>min_epoch][df.val_sensitivity>min_metric].idxmax()\n",
    "macc_idx = df['val_macc'][df.epoch>min_epoch].idxmax()\n",
    "val_idx = df['val_acc'][df.epoch>min_epoch].idxmax()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Sensitivity model: 0.926282018625 \t\tweights.0113-0.8270.hdf5\n",
      "Best Specificity model: 0.81280783773 \t\tweights.0127-0.7633.hdf5\n",
      "Best Macc model: 0.825920451026 \t\tweights.0142-0.8053.hdf5\n",
      "Best Val model: 0.83624524541 \t\t\tweights.0161-0.8362.hdf5\n"
     ]
    }
   ],
   "source": [
    "weights = dict()\n",
    "weights['val_sensitivity'] = \"weights.%.4d-%.4f.hdf5\" % (df.epoch.iloc[sens_idx]+1,df.val_acc.iloc[sens_idx])\n",
    "weights['val_specificity'] = \"weights.%.4d-%.4f.hdf5\" % (df.epoch.iloc[spec_idx]+1,df.val_acc.iloc[spec_idx])\n",
    "weights['val_macc'] = \"weights.%.4d-%.4f.hdf5\" % (df.epoch.iloc[macc_idx]+1,df.val_acc.iloc[macc_idx])\n",
    "weights['val_acc'] = \"weights.%.4d-%.4f.hdf5\" % (df.epoch.iloc[val_idx]+1,df.val_acc.iloc[val_idx])\n",
    "print(\"Best Sensitivity model: {} \\t\\t{}\".format(df.val_sensitivity.iloc[sens_idx],weights['val_sensitivity']))\n",
    "print(\"Best Specificity model: {} \\t\\t{}\".format(df.val_specificity.iloc[spec_idx],weights['val_specificity']))\n",
    "print(\"Best Macc model: {} \\t\\t{}\".format(df.val_macc.iloc[macc_idx],weights['val_macc']))\n",
    "print(\"Best Val model: {} \\t\\t\\t{}\".format(df.val_acc.iloc[val_idx],weights['val_acc']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_pred = dict()\n",
    "test_pred = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['val_macc', 'val_specificity', 'val_acc', 'val_sensitivity']"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checkpoint loaded:\n",
      " /media/taufiq/Data1/heart_sound/models/fold1+compare 2018-04-17 22:03:55.445492/weights.0161-0.8362.hdf5\n"
     ]
    }
   ],
   "source": [
    "metric = 'val_acc'\n",
    "checkpoint_name = os.path.join(model_dir+log_name,weights[metric])\n",
    "model.load_weights(checkpoint_name)\n",
    "print(\"Checkpoint loaded:\\n %s\" % checkpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load test data and evaluate\n",
    "feat = tables.open_file(fold_dir + 'comParE' + '.mat')\n",
    "x_test = feat.root.testX[:]\n",
    "test_parts = feat.root.test_parts[0,:]\n",
    "x_test = np.transpose(x_test)\n",
    "x_test = np.expand_dims(x_test,axis=-1)\n",
    "x_val = feat.root.valX[:]\n",
    "x_val = np.concatenate([x_val,feat.root.trainX[:]],axis=-1)\n",
    "y_val = feat.root.valY[0,:]\n",
    "y_val = np.concatenate([y_val,feat.root.trainY[0,:]],axis=0)\n",
    "val_parts = feat.root.val_parts[0,:]\n",
    "val_parts = np.concatenate([val_parts,feat.root.train_parts[0,:]],axis=0)\n",
    "x_val = np.transpose(x_val)\n",
    "x_val = np.expand_dims(x_val,axis=-1)\n",
    "for i in range(0, y_val.shape[0]):\n",
    "    if y_val[i] == 2:\n",
    "        y_val[i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D/3HrV/HO/rOYM+kSXPGVAqx/7RCO1l1Aj80pXyUD7kIWAIya7ps3TRuBl976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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efe00d85750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_test[163])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efe0129f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "predictions = np.argmax(predictions,axis=1)\n",
    "start_idx=0\n",
    "pred = []\n",
    "for s in test_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    # ~ print \"part {} start {} stop {}\".format(s,star_idx,start_idx+int(s)-1)\n",
    "    temp = sum(predictions[start_idx:start_idx + int(s)])/s\n",
    "    pred.append(temp)\n",
    "    start_idx = start_idx + int(s)\n",
    "plt.hist(np.asarray(pred))\n",
    "plt.show()\n",
    "print(163 - sum(np.asarray(pred) > .8))\n",
    "test_pred[metric]=pred\n",
    "# with open('/media/taufiq/Data1/Heart_Sound/ComParE2018_Heartbeat_/testPred.csv','w') as fp:\n",
    "#     for each in pred:\n",
    "#         print(each)\n",
    "#         if each > .8:\n",
    "#             fp.write(\"1\\n\")\n",
    "#         else:\n",
    "#             fp.write(\"0\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(x_val)\n",
    "predictions = np.argmax(predictions,axis=1)\n",
    "start_idx=0\n",
    "true = []\n",
    "pred = []\n",
    "for s in val_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    temp = sum(predictions[start_idx:start_idx + int(s)])/s\n",
    "    pred.append(temp)\n",
    "    temp = sum(y_val[start_idx:start_idx + int(s)])/s\n",
    "    true.append(temp)\n",
    "    start_idx = start_idx + int(s)\n",
    "val_pred[metric] = pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "def threshMacc(true,pred):\n",
    "        macc = []\n",
    "        eps = 1e-09\n",
    "        thresh = np.r_[0:1.:.01]\n",
    "        for each in thresh:\n",
    "            pred_thresh = np.asarray(pred) >= each\n",
    "            TN, FP, FN, TP = confusion_matrix(true, pred_thresh, labels=[0,1]).ravel()\n",
    "            sensitivity = TP / (TP + FN + eps)\n",
    "            specificity = TN / (TN + FP + eps)\n",
    "            macc.append((sensitivity+specificity)/2)\n",
    "        return (np.asarray(macc),thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QIhspBV9/reftt12IjdXQu3cCM2ca8fDIxE4emumk8ufnVvMWKA+ZQv80SXfM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efe02142290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, accuracy_score\n",
    "fpr, tpr, threshold = roc_curve(true, pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# method I: plt\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1axp1PiGEuFDERQcBcKygkku6NHyZaoPOQJuAGI6W51BZW0WgIaCpQ6zDYzuS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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efe021b9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(threshold[1:],fpr[1:])\n",
    "plt.plot(threshold[1:],tpr[1:])\n",
    "plt.plot(thresh,macc)\n",
    "plt.ylabel('True Positive (Green)/False Positive Rate (Blue)')\n",
    "plt.xlabel('Threshold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i8gbdfXr4yySQSSf3G/m2ZSq7ZUQiEe5ZMwfnr7aPOX10yLyYUEglIlwc5Q5ozsREQEQ+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efe00ce2c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "macc,thresh = threshMacc(true,np.max(np.vstack(val_pred.values()),axis=0))\n",
    "plt.plot(thresh,macc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(682,)"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.vstack(val_pred.values()),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(test_pred)\n",
    "df.to_csv('/media/taufiq/Data1/Heart_Sound/ComParE2018_Heartbeat_/testPred.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Validation Predictions, get per recording Confidence and append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15511/15511 [==============================] - 1s 96us/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(x_val, verbose=1)\n",
    "y_pred_hard = np.argmax(y_pred,axis=-1)\n",
    "predicted_confidence = np.asarray([y_pred[i,j] for i,j in enumerate(y_val_cc)])\n",
    "start_idx=0\n",
    "pred = []\n",
    "for s in val_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    # ~ print \"part {} start {} stop {}\".format(s,star_idx,start_idx+int(s)-1)\n",
    "    temp = sum(predicted_confidence[start_idx:start_idx + int(s) - 1])/s\n",
    "    pred.append(temp)\n",
    "    start_idx = start_idx + int(s)\n",
    "dfVal['conf_'+metric]=pred\n",
    "# plt.figure(figsize=(10,10))\n",
    "# dfVal['conf_'+metric].hist(bins=20)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Training Predictions, get per recording Confidence and append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93942/93942 [==============================] - 9s 98us/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(x_train, verbose=1)\n",
    "y_pred_hard = np.argmax(y_pred,axis=-1)\n",
    "predicted_confidence = np.asarray([y_pred[i,j] for i,j in enumerate(y_train_cc)])\n",
    "start_idx=0\n",
    "pred = []\n",
    "for s in train_parts:\n",
    "    if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "        continue\n",
    "    # ~ print \"part {} start {} stop {}\".format(s,star_idx,start_idx+int(s)-1)\n",
    "    temp = sum(predicted_confidence[start_idx:start_idx + int(s) - 1])/s\n",
    "    pred.append(temp)\n",
    "    start_idx = start_idx + int(s)\n",
    "dfTrain['conf_'+metric]=pred\n",
    "# plt.figure(figsize=(10,10))\n",
    "# dfTrain['conf_'+metric].hist(bins=20)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(10,10))\n",
    "# sns.distplot(dfTrain['conf_'+metric])\n",
    "# plt.xlim(0,1)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L89RTTyVJZs2aldmzZ2fp0qW55pprMmfOnPTr1y8jRozIxo0bkyTr1q3L+PHj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\n928IAAAAcBhq/+2W/D9jpmb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe922efcd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "devel_0029.wav 1 1\n",
      "devel_0033.wav 0 1\n",
      "devel_0056.wav 1 1\n",
      "devel_0072.wav 1 1\n",
      "devel_0076.wav 1 1\n",
      "devel_0129.wav 0 1\n",
      "devel_0149.wav 0 1\n",
      "devel_0156.wav 0 1\n",
      "devel_0174.wav 0 1\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "mask = dfVal['conf_'+metric]<=confidence_thresh\n",
    "sns.barplot(x=dfVal['conf_'+metric][mask],y=dfVal.filenames[mask])\n",
    "plt.show()\n",
    "for names in zip(dfVal.filenames[mask],dfVal.true[mask],dfVal.quality[mask]):\n",
    "    print(\"%s %d %d\" % names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vmTlzZm666abceOON2WabbT5xbFM+twjYmlm3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\nABTw/wA8XM7ydDG2lQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe922e8a850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a0357.wav 1 1\n",
      "b0226.wav 0 0\n",
      "d0004.wav 1 1\n",
      "d0023.wav 0 1\n",
      "train_0095.wav 0 1\n",
      "train_0282.wav 0 1\n",
      "train_0458.wav 0 1\n",
      "train_0475.wav 0 1\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "mask = dfTrain['conf_'+metric]<=confidence_thresh\n",
    "sns.barplot(x=dfTrain['conf_'+metric][mask],y=dfTrain.filenames[mask])\n",
    "plt.show()\n",
    "for names in zip(dfTrain.filenames[mask],dfTrain.true[mask],dfTrain.quality[mask]):\n",
    "    print(\"%s %d %d\" % names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find out the recordings that have huge fluctuations in confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "93942/93942 [==============================] - 9s 95us/step\n",
      "93942/93942 [==============================] - 10s 107us/step\n",
      "93942/93942 [==============================] - 9s 97us/step\n",
      "93942/93942 [==============================] - 9s 97us/step\n",
      "93942/93942 [==============================] - 9s 93us/step\n",
      "93942/93942 [==============================] - 10s 106us/step\n",
      "93942/93942 [==============================] - 9s 97us/step\n",
      "93942/93942 [==============================] - 9s 99us/step\n",
      "93942/93942 [==============================] - 9s 97us/step\n",
      "93942/93942 [==============================] - 9s 99us/step\n",
      "93942/93942 [==============================] - 9s 98us/step\n",
      "93942/93942 [==============================] - 9s 98us/step\n",
      "93942/93942 [==============================] - 9s 97us/step\n",
      "93942/93942 [==============================] - 9s 98us/step\n",
      "93942/93942 [==============================] - 9s 98us/step\n",
      "93942/93942 [==============================] - 9s 98us/step\n",
      "93942/93942 [==============================] - 9s 95us/step\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(min_epoch,df.epoch.count()):\n",
    "    checkpoint_name = os.path.join(model_dir+log_name,\n",
    "                                   \"weights.%.4d-%.4f.hdf5\" % (epoch+1,df.val_acc.iloc[epoch]))\n",
    "    model.load_weights(checkpoint_name)\n",
    "    y_pred = model.predict(x_train, verbose=1)\n",
    "    y_pred_hard = np.argmax(y_pred,axis=-1)\n",
    "    predicted_confidence = np.asarray([y_pred[i,j] for i,j in zip(range(len(y_pred)),np.nditer(y_train_cc))])\n",
    "    start_idx=0\n",
    "    pred = []\n",
    "    for s in train_parts:\n",
    "        if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "            continue\n",
    "        # ~ print \"part {} start {} stop {}\".format(s,star_idx,start_idx+int(s)-1)\n",
    "        temp = sum(predicted_confidence[start_idx:start_idx + int(s) - 1])/s\n",
    "        pred.append(temp)\n",
    "        start_idx = start_idx + int(s)\n",
    "    dfTrain[\"weights.%.4d-%.4f.hdf5\" % (epoch+1,df.val_acc.iloc[epoch])]=pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "80\n",
      "0\n",
      "1\n",
      "80\n",
      "2\n",
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": 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qz5MjIiIicrOr9BRhUFBQqT+EeqnFixcbXY+IiIjIDe9/96IGERERkWtEAUtE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\nwMlfHeivs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60f8ed0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list_sweep = dfTrain.columns[-(df.epoch.count()-min_epoch):] # list of columns for sweep\n",
    "# plt.figure(figsize=(20,10))\n",
    "# sns.boxplot(x=\"filenames\",data=dfTrain[list_sweep.append(\"filenames\")])\n",
    "# plt.show()\n",
    "# dfTrain['conf_mean'] = dfTrain[list(dfTrain.columns.values)[-100:]].mean(axis=1).values\n",
    "# dfTrain['conf_var'] = dfTrain[list(dfTrain.columns.values)[-100:]].var(axis=1).values\n",
    "metric = 'val_sensitivity'\n",
    "plt.figure(figsize=(10,7))\n",
    "for color,dataset in enumerate(dfTrain.dataset.unique()):\n",
    "#     if dataset != 'f':\n",
    "#         continue\n",
    "#     sns.distplot(dfTrain['conf_'+metric][dfTrain.true == 1][dfTrain.dataset == dataset],bins=20,kde_kws={'bw':.04},label='Abnormal')\n",
    "    sns.distplot(dfTrain['conf_'+metric][dfTrain.true == 0][dfTrain.dataset == dataset][dfTrain.quality == 0],bins=20,\n",
    "                 kde_kws={'bw':.04},label='dataset '+dataset,\n",
    "                )\n",
    "    print(dfTrain['conf_'+metric][dfTrain.true == 0][dfTrain.dataset == dataset][dfTrain.quality == 0].count())\n",
    "plt.legend()\n",
    "plt.xlim([0,1])\n",
    "plt.xlabel(\"Confidence for true class\")\n",
    "plt.title(\"Training Recording Confidence for Abnormal PCG (Best \"+metric+\" Weights)\")\n",
    "# plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 1 : Count 67\n",
      "Label 0 : Count 164\n"
     ]
    },
    {
     "data": {
      "image/png": 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CF4RxFgzHONbgRqdI3DinYNyGKJAkiSUz83hr/0VqmwNUlCV/Hx6Pm131ezBbLAmvE/D7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\n41hNlLr6KHNmjewqX5ZkPj/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rzAJNN6FYmKeOPYs77OFTlZ+gMnP6eIc0JJ9fZc/7YVqd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\nCV+SJKmf+H9ez8wY6N0RagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60f2ef44d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for true in dfTrain.true.unique():\n",
    "    print (\"Label %d : Count %d\" % (true,dfTrain['conf_'+metric][dfTrain.true == true][dfTrain.quality == 0].count()))\n",
    "    sns.distplot(dfTrain['conf_'+metric][dfTrain.true == true][dfTrain.quality == 0],bins=20,\n",
    "#                      kde_kws={'bw':.04},\n",
    "                     label=str(true)\n",
    "                    )\n",
    "plt.legend()\n",
    "plt.xlim([0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = {'a':'b','b':'g','c':'r','d':'c','e':'m','f':'y','x':'k'}\n",
    "markers = [\"x\",\"o\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60f25262d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EOwDgwOEDKBRfDKDX7Tq5PAednZ0+75mEJKQhDVOyfubc+dkyshXQ9p9TML4Q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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60f2ecee90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BBgBw6XRQBuTSuVK8xlogp8UBZ6NDcE6qDQLcNSo9BmthtLzbjJMPH4ezyQH1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\nOhZ3RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60fe7935d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NSr+zYlzH5h6DNU8kElhkCU6nzNLQz6efOlUFo9GIq6++NuTvIiIi8ka4Y7Pt4EX7ZgPH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\nLoqDQjXcuURIQSI0mu6SHDk5lqhbqxa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O1vbs2YO1a9di6tSpmDVrFq666qpQjouIQsRb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\nEvR5vsyFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6140196f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F7bmvv/4qtm59HSaTCcXFxZgz50b83/97FzIyMuI2BiJKTaGyZIFqvIJl8NxZ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\nOBhhDQAAwMH+H5KtC21mD5UEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60fefb8350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UvddN6g1W22KwJlusuPhh/0Ae4K7TQDBY82LkU2PR+NBpWM5bkPrtIcj6nyCL\n3ARgzJhCKBQKnD79Nb7znSmS34+IYkew05eun5swYRzy8vIEgZK7TQCeugkAgF5/GkcAIF+GB3Iq\nMLpxNI7rj/e7r9VqhV5/GosWLcS/Dn4GQ3k9GrWNWDZ9MT5q+1Bwbuq3hqBw56V949WsxP79+zx+\np4FsIEgQBafiYwo195vzuOvUfwzWvJCnyDH8/v7ZLSmlpKTg5z//X/zxj08AAK6++tvo6enByZNf\n4cSJ41iy5K6wjoeIokew05fiz82adSNKS+cJMl3uNgE4ru0p83a64zTUu8bhcWzHquXL8dUHX+Lc\nxSY0mhsF5zU3G51r3DZho2DXp4NrxwJPGTWlMgUyGZxr1lwZjUB5uRI6nRxqtRWrV5uwcWPfcWWl\nCarevRA9anVvRs1xXODlp0cDlTolDe37XMqyZAAZ0zO56zQADNai0KJFt+GSS7Lxyisv4okntiI5\nWYlRo0Zj1qzZkR4aEUWQv9OX4iyZVvuV4H2DwYD9+9/x+9riNWcOOS3ZMJTVY9T2Qjyz888AgJKS\naWisFQZrQ4dmOqe8PE2Zuu7gFI8lJSUF06bNACCDwaCHWq127jh1LF4/UW3GlS2pOIAJqK1NQk1N\nAvR6ex2R2lp7yaPt2+3rpNp7NyTY16wVoD0ONiREsxFbC2BQ+Nc/ltxjsBalZs8uxezZpZEeBhFF\nEX9LVIgzafn5I0TX6f85b9cW994EgGxkYwVW9Ft35C6wmzTpciiG2OuG5SEPxyGcMpXL5c5OBOXl\nK1FXd1LwfknJLAAQfKeOmovYeehvzozdcADDcRE2ABWYhOZm4dSbTtdXAM6myuIatTCKp52tkcJg\njYgoRvhbokKcmXLsILeXvVChvb0NJSXTBGvTvF1bpcrCoUPvoaxsJb6sPo6cFnugloEMwbojo9GI\n9vb2fuM5f/488p4cjcSaj7FWfwM+wLvoQo/z/UsSLkF7WTPu774fr+171fl6ZmYmiotnoLJyM26+\neZ7gmvV6HQxl9f2CxTyYesdsQ2dnX8CmVlvd/qzCwdhpRPnhldC11kGdoUZl8RaolPG9+9RTc3gK\nDoM1IqIY4W+JCnF268KFFmdWrLOzE3q9fX2YuwK19gBuc78F/I57W5rNziBJoVYgefUQaDSLoNPV\nobHxrCD71jeeAiSqkpB/6AeYULYStccn4Pq6OpzrtiHdmo7N5s1orWrBV5nCLgUFBWM99hXNQ55z\nDK6V/k35Yw6pAAAgAElEQVSZySgtNmPNGhM2bBCuWYuU8sMrUaXtzQo29a4H/MGOiI0nHNgcPrQY\nrBERxRlxlkyr/dJtEAUAddpTuGvGYvxTvx9A/80F7jIkrn/oajSLPJbXAOzZMUeWzjH9OAzApwC0\nJccEgdZwDMcX+Nx53Nh41pkBXLNmPTpqLqJer0Me8rACKwRlPhzj+2llPm5R2QMzxxq1SNO11nk9\njkdsDh9aDNaIiOKMOAOn0dyKo0ePuD03x5iNer1O8NqhQwcwYYI9CLoy+UqsPLsSGchwmyHxVaOt\nuHhGvyyd0WjEihV34v8+fxeADVfgCpShDPdNWYt0xVBBlk6vP+0MIHce+psgq+eYWov2jI06Q92b\nUXMcF0RuMGHC5vChxWCNiCiKhaI1lGumraBgNLq6zDAYDFCrC7BEewce0m8QLPpvbe2rNl+Nasgg\nc7aCcmRIPG0GyM0djq4ue0bL0RtUrLx8Jfbt6yu98R7ew+P5T2Dn1r9hu2oHAPuuUtds4P799ViC\ndFRWFjpLcMSKyuLen39rHdQZBagsjv/dp2wOH1oM1oiIwiyQACwUraFcM23Z2eloampzjmHl1yug\nQiaykAUj3HcFaEAD7sf9MMCA0Y2j8Xjz9n610DIzMzFlyrVwLa/h6Xu5y8Y15ZxzLkA3GoHGxrGw\nT5badY4ZhqoffIaaQwq8ct0IbFyT7raGWjRSKbPCukbN38X9Um4CiIWMZyxhsEZEFGaBBGCBtoYS\nE/+BnPncpH5jAIB0eTrgYcNkK1qdhWyP64+jrGxlv3EUFIyFQpHs1/dyV97DtVRIebkSev3TAOQA\nTgGXZQEblwIZbdADmPcuoK+a3HsfYQ018n9xPzcBxA4Ga0REYRZIAOZvbTVPxH8gn1hyAjmPj+53\nz4SMBKDF5Rj2IEgBRb+Mm05Xh1HDR6HWJfM1Oq//NT19r8rKLeju7sYHH7wHoP90qb0mWhaAXfYX\nln0CZPT1l2xWdonuIwf18XdxPzcBxA4Ga0REYRZIAOartlpfk/WvYDSeR1ZWFgoLxzmnIMV/AJtO\nmdyOYcqU70GhUDjv03XRhDfffgOd6HQz/gLc03032nABBhiQhzwsx3I8qn7Ur++lUmVh586/efzO\narXVmTEDAGVLMkzoC9ZUpmTBqCJZQy0a+bu4n5sAYgeDtSj11FOP4403XkdLSzNWr17HVlNEccTf\n4raA59pqjiCtuvogWlr6UmJ6/enenZ/2KUjxH8hJeUlo0GixRLsEHfkXoR9qQPMFIxoadCgsHIdd\nu16BSpWFkpJpbsfjKMVhvPmsc9MBACgNKajc5f/3AgCtFpg/Pw3NzTKoVDbs3n0RY8YAS5aY8NZb\nCejqkiE52Ya/Xp2Pv2RYoOvuhlqhwJqpI7Ch1BwVNdSikb+L+7kJIHbIbDabLdKDCFZTU5vvk2LQ\nF18cxe23/y8eemgzLrvscqSlpUGhiK+/8TgWOVPs4bOLLE9BmlhR0bewf/87/YrYJskScP7V887z\nHkh6AIfMh5zHpaXzsH37Dmg0t6Kqane/6zreb9BondOrAJBRmolR2wv7NVR3t/jfcc4bbyTAbO6b\nwszPt6K29iKKitKcfT1dXx/s+N9ebMvOTg/6s8ysRaGGhnrI5XJce+11kR4KEUUZ8cYATxxTkOJd\nefU3nhCcpzfrBceOdWaO7J9W+yWMRiOGDRuGsWPHObNlnrIy5eVKVFXZdxS6Lv53DeIaG2WCYMw5\nFr0MM2ak4swZYV9PcZ9PosGGwVqU2bDhfuzbtwcymQzXXXcNZDIZDh/+ONLDIqIo4c9uUNeuAWLK\nMUq01fRlZ8SN1R1BnkqV5WxDlZjYvw2Vp9IM4sX+jmPXIE4g3QwsPwHkmYAzShzdPAGwJghOUali\ndgKIKCQYrHnR2XkUZ8/eB4ulCSkpVyAvbyvk8mRJ77l8+a8wfvxEPPHEVuzevQ8A/ydFRH3EuzAB\nQKlMgcnUt+TeXdcAhwnbJqCry4z26jZYW3qwAisAAI2ZTRhfPFEQ5AVT4028OcCx+N/jjs3lJ4AZ\nTfZfX9pm/19exSQkJVmRmNi3lo1oMGOw5oXBsBydnfasVlfXEcjlKuTlbZD0nqmpaRgyZAgAQBXN\nVR6JCIB/BW4dU4CN2h7cavwSk4Z1InVscEVIV8hW4B0cRJvL7shx48ahsHC8Xwv7k7LsGTHHWjal\nLhW/V29xO5Zgarw5FvuLF//32+GptMJkktszaq56j2+4oYe104h6MVjzwGYzw2wW9sszm+siMxgi\nigruAjN/sk+OKcC1OIGJOA+LHmg9ElwR0q6GLiihFARrY0cWYh3WohvdUEABc7MZmrJFXgNIfyrM\ni8t7NDaORXMzvHYLUKncF6gVB3Fr1pgwd24a9GeU9oxar4yOZEwvNXOHJ5ELBmseyGRJUCgKYbGc\ndb6mUEyI4IiIKNLcBWb+ZJ8cU4B5EAYg3brugHt/bjb+Hk1och7nKnOxXLZcUPh2Q005/qnfLxhn\noC2qAPsmg5qaBOj1OgBjoNdvw/TpacjJsQXc5sldEJeTY4N+8wT71GeeCZmmZHy0MB+q/2GgRuSK\nwZoX+fmP4+zZtbBYmqBUFiE3975ID4mIIshdYOZPgVvHFOAZKHGpS0ZMoVYEvC5MP1QPuGzgvGRM\nNlIMKTChr5ba180NXsftL5UqCzk5f4de3zd9qdfb/6mtTUBNTQKysmwwGmXIyrKhsNB7qQ5xOQ/7\nzyUJqLC3wCouNUN1CwM1IjEGa14kJ4/D6NGeq2wT0eDiLjCzrw+zZ9jy8vLQ3d2NkpJpgiyZY0rv\nn9pC5ButmDSsE90jO3B/9/345ztvCe7hK7BqvtDc71hxjbDw7UjVSBzrPOY8bmw8i+Zmo8e1dO5q\nojkyfnV1OgCFALbB3gKqj14vh17v+DVw9GgChnSfxzOKO5Ggq0OPWo32yi0oL893W87D0/o2IhJi\nsEZEhP7r0VavXo+NGx8QTE+66zzg2mFAo1nkNksmnAIscJ77WtWr/cbhq/fnsGHDoNefFhyLa55t\nXbMN188tdp6n159GWdlKj2vp7OMVNkQX1nP7BBkZJnR3J8NkqgMwBu6CNwCY9/bdUJrtn0uq/RTa\nd7/AP5e/APzABpxRApsnOKeFPa1vIyIhBmtRaNas2WwvRRRm4unImpqPncGOa+DlbYoykN2T4vdS\nUlJQUjLLZ4umsWMLceTIZy7H49xuFsjJyRUEdd7W0rk7Fp8vlx+GyeToWFADpdIGk+nFftccKdqI\n9cCdP0HHda32g97SHOrPuP6XKBAeCt8QEQ0u4uDkzBmD1/fdUavVouMCv89NTp4N4O9wl61yVVm5\nBaWl81BU9C2Uls7zGNyJr++YChWeY/V4LP682PjxWpSWmpGRIbzGKYwRHuflCY4zL+vgdCdRgJhZ\nIyJC//VoVqs4kCnweQ1306TNWi1+M382TjUbUaDKwsbde5E5Zqzz3OpqHVpaCtHSss05JeltatBT\nY3d3Y3HNDjqmQl999WUAgMVoxq+7tfhpphkGpODDKWNRUdkDwD4l3N3djczMTADAlCnfA2DDvn17\nndcfO7YA27ebUFKSitravvvegW2YgvcxGl8DAMacOYOaSy91vl88IcnvHaREZMdgjYhiVqBlL7xx\nDbTq6k4KmqR7a9/kyl0gtXT6tXipN2D6pPM0bKU34TffeQ3dum6sU6/FspHfwActSuf5Hiv9B8i+\nk9PzVKihvB6mfS0YDmA4LqJYYYFKZZ9KLS9fKQjMFAoFKis3Q6FIhk5Xh+HDC9Dd/SRKSlLR2Cjs\n25man4lbsj5FxbmlGN5xCvOf1eLD4T/C0Mk9GJuqQGXe6JB8P6LBhMEaEcWsYNoheSLcKHArqqp2\nO9/z1r7Jl1OiqcfjjRDURLs1/0t8gMnO98VTkwPhraxIt65bcK7rsbu1d/ZeoTtQXq5EdXUCWlr6\ngsrhw60w9SYDr7yyB1u3KqFSPQcAmA7gX6H5OkSDFoM1IopZwbRD8oe76cxgFaiy8ElnX3YrL2EE\n4BKPTRrWidJrzJKUrxB/j9Wr1+Hmm2/GiRNfIbtxGJZhGTKQAcBe883BU5DnqRl7RwfQ2moP3vbt\nk0Oh4C5PolBisEZEMcufgrTB8HddmC9GoxEdl02CqrERNmsPrsvOwYrLK4ADfeekjlVIFtiIv4dr\naREAQL4MD+RUQKG29yl11F3Tap9Gfn4Chg07ibFj+4JVT1O0nZ3C41BN5RKRHYM1IopZocyAhYrr\nOrrGxrOCNWPy//ouxq2+ArpjX6Kn2YIEVSKy1+QP+J4WoxmG8npnnTVHU3Zx0duTJ+sEn2vKOYfC\n/X2L/8s1jsxZNoB/4JprzM5A0mhEv/VpDikpgNncdxzKqVwiYrBGRDHMVwbMUxAjJWExWSGdrg5N\nG/Ww6O2RjaXTjKYN+oCbuYsZyuvRUKXDVmyFodaA0TWj8fih7f06B+TnjwXgORPpre7aihVK6PXu\nM2bXXtsDhaKHnQiIJMJgjYjilqG8XrCYH8CAAyNfvK2bU6sLvC7sD1a3rhtbsRXv4B0AwHH9cZSV\nrYROJyxam5X1JL73PTm++OIrGI1jodU+CY1GKerV2dcHtLFRhpKSVKjVVrz3XoLgWklJVkyaFHhD\ndyIKHIM1IopbUgRGvojX0eXnj0BOTq5zmra9rFnQx9N1YX8wjEbg340pMKB/EV9x8FVYmIldu3Zh\nzhwzjh5N6u3naX9P3KuzsVHm7P1ZW5uAxESb4PppacD+/R0gIukxWCOiuKVQK0IaGPnDU/9Qh/TK\ndAAQTM0ORHm5Egf038BoFAA47nxdrS7AxtVtmFVzGsrmLphUyZi6ZgSALI/Tna69OktKUp1N2gEg\nNdWG1ta+NWtTpvQMaNxE5D8Ga0QUc/wthuva4LwjrwP3d9+P+pL6ARfQ9cbXOrpWWxseQAV0qIMa\nalRiC1S9LaaCWWOn08nRjgR8gRcALEFKykmUlIzG6tWbcXjeaUzUn7ef2NmOzg09wKtZ/TJu7jYE\niM/hujSiyPE7WKutrUVRUZGUYyEAra2tWLToZygunoF77rkXANDcbMSiRQtx440/wuLFSyM8QqLw\n8RSU+VsM17XBuUazCK9VverzM57HItxZ6bpOK5BOCt7GHugaO6PRiMbGXwDQARgDYBtKStKxfbsJ\nGo0SP9CfE5x/otqMO74NXHIJMGuWGQaD/busXm0/3/W7uU6Jcl0aUWT5HaytX78eCQkJWLhwIW66\n6SYkJydLOa6o0Np6AQ8+eD/On2/CVVd9G0uWLINM5n7reqhkZGRg/foHcc89S3DNNd/Bd7/7PVRU\nrMOIESNx2213SHpvomjjKbAJphiur88YjUYsX74SH35oD3ymTHkcW7emOgMU14KwjoyTY8rQ3+DR\naASqq3UexxHoGrvy8pXQ6x07T2uQn29FZeVzvdeV4wyUuBRtzvO/aElFTQ0AJKG01Oxcc6bRuP9u\nLGxLFB38Dtaqqqrwr3/9Cy+88AK2bt2Km266CQsXLsSoUaOkHF9E3XHHbXj77bcAAG+8sQdWqxXL\nlt0j+X2vvPKbWLToNmzY8Fv88Iez8Z//HMOOHS9ALmehSRpcPAVYwRTD9fWZ8vKVePNNR+DzSW8l\n/r86AxZvZS38DR7Ly5VoaSkE8InbcQS6xk58n5yck87gUq22YnPtBNgA5MGE80nJ2GKe4GH8nr8b\nEUVeQGvWrr76alx99dU4duwYlixZgp07d2Lq1KlYtWoVCgul3Q4fbmazGUeO/Nt53NPTg08++Ths\n97/11l/io4/ex4svvoD779+AnJzcsN2bKFp4CrA8FcP1Nh3pq4Bu/wDrFHQ6ufOadXU6AIUAtgHI\nEqzzGjV8FGpd6peN9tCs3B4EbXNePzOzAJWVDzvfd11j58/mA28BaGWlCWVQ4nXdN6BWW9HdDbTv\nS3I51yr4ta81bBR9IlFHkCIjoGDtX//6F55//nn8+9//xoIFC/DjH/8YH374IZYuXYq33npLqjFG\nRGJiIoYNuwRnz55xvqZSDQvb/c+da0JDQz3kcjnq63W+P0AUI7wFVOL31qxZD3cBlqdF/N6mI30t\n/BcHPsAYqNVWUZHbT5CZaUNx8fMoL2vGn39cgxc//T2aLtYjG9nIRCZGYASWY7mHe1hRW5sFYBcA\noLjYDJWqb6rRdY2dP7wFoK47OwGguRlQKAC9Pgn5+WbBBgF369NiyWANWiJRR5Aiw+9g7aabbkJa\nWhpuueUWPPzww0hMtH+0tLQUr732mmQDjBSZTIb16yvw29/+BufOncPll1+OtWt/G5Z722w23H//\nfRg/fiLmzFmA9etX46qrvo3LL58clvsTSclbQOXv2i9PBtLYvbJyC7q7ZfjgAx2AsZgy5TFUVppw\n883CaxQUaLF9uwmv/PeX2FP9CD7uLUQLAJMwCeuxHkpDitsNCYEERd42NDgE0sPUEbxlZyehqcnk\n9r1YNViDlkjUEaTI8DtY27BhAyZPdh8sPPvssyEbUDSZPn0mpk2bge7u7rBuqNi581nodHXYufNv\nyMoahh/9aC7uv/832LHjBaSlDQnbOIik4C2gGkiwBQyssbtKlYWdO3cAcGT4luLmm+39PYX3sF9T\n+bWlXyFax7FCrfC4IcHfoMjbhgYSGqxBSyTqCFJk+L2KdMOGDf1e+9nPfhbSwUQjmUwW1kDt6NHP\nsHPns1izZh2ysuzTrsuWrcCQIUNQWfm7sI2DSCpqtVp0XODXe/6orNyC0tJ5KCr6FkpL52H16rXQ\naBahpGQaNJpb0dxs9Os6jgxfbe2n0OtPIz9/hMs1N0OjUaJBkYQ85Ak+NzJzFDJKM5FXOTqoRftG\no31nZklJKqqrhe2dAv28RqNEc7MfXzYOiIOUwRK05FWORkZpJpRFqc7fdxSf/M6smUzCv9FZrVZc\nuHDBr88ePnwYGzZsgM1mw/z587F48eJ+57zxxht44oknIJfLMXHiRDzyyCP+Di2uXH75FTh06APB\nawqFAn/60wsRGhFRaHlbZ+VrE4Av4mlBjWZRUNOq/XdZ5mL//nd6r2nPeB1ILcIdE8rQdlqGc3ID\nLr12Ah599DHn+jtfi/bdTXO6ZtPE/Fn0P1izcYFuzIgXga5xpNjlM1h75pln8Mwzz6C9vR1Tpkxx\nvm4ymXDTTTf5vIHVakVFRQV27NiBnJwcLFiwADNnzhTsHtXpdHjmmWewa9cuDBkyBEajf3/7JaLY\n422dVSBrsPwR7LSqt+lUR4arvSMVj5yYhaKiEtS46ZHpaX2aI0irrk5AS4v9Wo7ASpw9y8y0oqDA\n5vei/8FagoNBC8U7n8HazTffjB/+8IeoqKjAunXrnK8PGTIEQ4cO9XmDzz77DGq1GiNGjAAA3Hjj\njThw4IAgWHvxxRexcOFCDBliX4+VlRX6FjBENPgEu4ZNnOFzTH06GpwL7+E+4+Vp0b6n7NnJk3KM\nHSvMxhUX9wSUGWMJDqL45DNYS09PR3p6Op5++umgbnD27Fnk5fWt68jNzcWRI0cE59TV1QGwr4Gz\n2Wy48847cd111wV1PyKKD8GWY3At/5GXl4dZs26EwWAIaFq1/3SqMMDKz7ciJ8f/jJcrT9mu8+dl\neOmlgZXQiPUSHETkns9gbdWqVXj44Ycxf/58t62WXnrppQEPoqenB/X19Xj++eeh1+txyy23YM+e\nPc5MGxENPsGWYxCW/wBKS+c515sFw94iSrjYPyfHhr//vQPl5UrcfHNqQL0zxdkvh6ws24BLaMR6\nCY7BbLDWiiP/+AzWbr31VgBAeXl5UDfIzc2FXq93Hp89exY5OTn9zikqKoJcLsfIkSNRUFCAuro6\nXH755V6vnZ2dHtSYKDrw+cWuYJ/d+fPnsXTpUpw6dQpjxozBtm3bPC57qNf3CI5t+h6/7qvXN/Q7\nHsjvtWXLgJYW4WsTJiRg3bp0VFXZj2trE5CcnIRdu3xf77nngCVLgH/+E4LdmpddlhC2/yb43170\n+XzZ54K/nCQnJ2HSrkn9zuOzG5x8BmuOgEkul+Pqq68O+AaTJ09GfX09Tp8+jezsbOzduxebNwun\nIr7//e9j7969mDt3LoxGI3Q6nV89R5ua2nyeQ9EpOzudzy9GDeTZaTSLnVmvmpoadHVZPG4okOUn\n9Dv25775+SMB1Lgcj+r3OW9dFMROnEgF0DeWzEwrKiou4uabha+//roNc+ZY+mXY3O36fPxxe6BW\nVtb3ekWFCU1NPr/egPG/vejUduJiv2Pxc+Kzi20DCbT9Lt2xceNGtLW1Yc6cOZg7d65gHZo3CQkJ\nWLt2LX7xi1/AZrNhwYIFKCwsxGOPPYbJkydj+vTpuO666/Dee+/hxhtvREJCAsrKyvzavEBEsSWQ\n3ZnBlmPwp/xHIJ0SxNOWxcU9UKn6v97ZKXOua3OdivRUToNTlrFJqulKFrglb/wO1l5++WUcP34c\nr776Kn7yk59g/PjxmDdvHmbPnu3zs1OnTsXUqVMFr919992C41//+tf49a9/7e9wiCgGBbI7M9hy\nDP6U/wgkaPS0aN/x7/37E9HZ2bee11f5DMexP+2kKPpI1dpqsNaKI/8E1Mh94sSJKC8vx4oVK/Dg\ngw9i1apVfgVrRDS4eApEBlr0tu/6/k9juvuMpxZS7njKgDleF+8UFZfL8FROY7AWsI11UrW2Yq04\n8iagYO3EiRPYvXs39u7di3HjxmHTpk1SjYuIYpjnqb/QFL0NpuG762cAID9/BHJycgcUNAK+y2V4\nen+wFrCNdZyupEjwO1ibO3cuOjo6MGfOHOzatcvvNWtENPh4C0RCMf0XTGcCby2kBsKRYXN8L3Ep\nD0+ZucFYwDYeylNwupIiwe9g7b777sNVV10l5ViIKE54C0RCMf0XTGcC8WeGj8yH5q1F0LXWQZ2h\nRmXxFqiUnqdSjZ1GlB9e6fH8QL/XYCxgK9V6L6lYLEYYDCvR3V0HhUKNvLwtSFRlRfWYKT75DNYa\nGhowatQoDB06FF999VW/98eNGyfJwIgodnkLREIx/RfM2jfxZ7p/0IUqbe9UalPvVOoPdnjM/JUf\nXun2/GC/12DcDSrVei+pGAwr0dpqf+Ymk/2Zjxq1I6JjosHJZ7D24IMP4umnn8bixYv7vSeTyXDg\nwAFJBkZEsctbIBKK6b9g1r6JP1Pyj2mC93WtdQA8Z8gc74vPdxiM05qBirX1Xt3ddV6PicLFZ7Dm\n6Al68OBByQdDRPHLMaX0q1/VYdassdi580nk5GRGbPpPnaHuzZA5jgsA9M+IabVyaDRK1GWPBUb2\nP99hME5rBirW1nspFOrejJrjuCByg6FBze81a/fccw8effRRn68REbnjOqU0ceKnePxxq9cppWDK\ncwSisrh3WrS1DuqMAlQW26dSxRkyo1GGo0eTAOXTwGw5MsdoUXyF2nm+/RzWTPNHrJWnyMuz/x6x\nr1krQF5e8LuGiQbC72Ctvr6+32snT54M6WCIKH4FOqUUTHmOQKiUWfY1ar0bB25+fR7UGWqsqdgC\nIN8ZeGm1cuj1AExZwEu7UFDUg+2rOgTXYs20+JSYmMU1ahQVfAZrL774Inbt2oW6ujosWLDA+Xpb\nWxvGjBkj6eCIKLa5Zseys89i2TIgI8P+nq8ppWDKcwTD7cYBl6BQo1Hi6FHva9FYM42IpOQzWLv2\n2muhVqtRUVGBsrIy5+tDhgzBxIkTJR0cEcU2cSFaYAQeeCDXrymlYMpzBMPXxgF3a9HE0555ed43\nF3CalIgGwmewNmLECIwYMQJ79uwJx3iIKI6Is2FNTbkoLHzHr8+GqjWVL542Gji429nq2mKqtjYB\ns2aZUVpq9ri5gNOkRDQQPoO1hx9+GKtWrcLdd98NmUzW731uMCAiT3xlx4xGI5YvX4kPP9QBGIMp\nUx7H1q2pvZX/Q9OayhdPGw28EU9zGgxy7N/f4eHs2JsmjYdOA6HGnwlFks9gzdG1YPr06ZIPhoji\ni6/sWHn5Srz5pmOa9BPs2yeHQvHXsGadHBsNAhFoTbVI1mATBxmZz03y+Zlwdhrw1RkiWsRa9wWK\nLz6DtRkzZgCw9wYlovCTuoSFlHxlx/pvGjiFxsZmNDQsEbb4SYyu7xtoTbVI1mATBxknlpxAzuPe\n65uFs9OAr84Q0cLdz8RtO6oo+71K8cHvXPxDDz2EtrY2WCwWLFy4EEVFRaiqqpJybESEvkX6tbWf\noqpqN8rKVkZ6SCGjVqtFr4zBrbfeidbWV2AyfYrW1t0wGKLv+6pUwKZNJqjVVuh0cpSVKdHc7P38\n7dtN2L+/A9u3h3dzgTjIMJ3yHCgaO43QvLUIH8jeF7wuZacBXxs8pGI02tcelpSkQqPx/vyA/j8D\nhVrhrB0Yzb9XKT74Hay9//77SE9Px//93/8hNzcXb731Fp577jkpx0ZECF8JCzGLxYiGhkXQaqeh\noeFWWCzGkN+jsnILZs2ah8zMq5CZ+WPMmvUYJk0S1m+M1hY/jk0DtbUJqKpKQlmZMtJDckscZCjH\neB6nI8u17vq1OHjZQZwZcwYZpZmSdhpQZ6hFxwWS3ctVoM8vr3I0MkozoSxKdf5M2I6KwsXvorgO\nNTU1uP7665Gbm+t2wwERhVa4SliIhaOJtUqVhZ07hddsaFCjtVXY4sef0hfhLo8RK5sGxC2eJmyb\ngJYe99k1R1arPbUdFT+pQFH2t7D/x+9IOr5gNniEQqDPL1GVhLTHVHjAsb7uYzV+PTFPcA7bUZFU\n/A7Whg0bhvXr1+Pdd9/F4sWLYbFY0NPTI+XYiAjhK2EBCNfHZWWdxD339BWxDVfWwF2LnyVLfJe+\nCHd5jFhp3C5u8ZSUlQQ0uf+5+CpjIoVgNni4CnaXZjDPT7y+Lll2I8ovncd2VCQ5v4O13//+93jt\ntdcwd+5cDB06FF9//TX+93//V8qxERHCV8IC6F/E1moF1q+3/zpcWQN3LX78yYKEO9MVj43bI5Xl\nGhsXgS0AACAASURBVIhgd2kG8/zE6+lOXDBg1Kh3AhswURD8DtaysrKwaNEi5/HIkSMxcuRIKcZE\nRBEiXg/X2JgJpXJsxLMG/mRBwp3pclcsN9YNNMsVCcHuXA3m+UUi80gEBBCsffrpp3j44YfR0NCA\nnp4e2Gw2yGQyfPDBB1KOj4jCSLw+bvz4GSgs3BG5AfXylgVx1Ok6ObMO+QVjkfX+kyjMz4yLTBf5\nplArnBk1x7FUYjHzSPHB72DtN7/5DZYuXYqioiLI5dG5kJaIBiac6+MC4S0L4rqOCKpPcc3d1pjL\nDlHwxBsopNy5GouZR4oPfgdrSqUSN910k5RjIaIIC+X6uHAVDA1FnS42Wo9d4g0URPHI7xTZ1KlT\nUV1dLeVYiCiOBFIwNNACpa581eny59qxUjONiAYnvzNru3btwtNPP420tDQoFAquWSOKItHYkiqQ\ngqEDKbvhax2RP9eOlZppg4G738vp6WBbJxrU/A7WXn75ZSnHQUQD4Fpyo7b2U1Q3HELxiukRbYqt\nUKh7i+k6jj0Xtx1IsKRSZmHT1M3OZuBl1SsE39ufa8dKzbTBQPx7GZBh3TpIXqCZKJr5HayNGDEC\n7e3t0Ol0mDRpkpRjIhoUgi3m6Y645EbLmRZUaXcjkk2xAyluO9BgyVszcH+uHY8102KVu/Zq3aJq\nHGzrRION38FadXU11q1bh4SEBBw8eBBHjhzBE088gaeeekrK8RHFFdcpnuzGYVimX4YMZARUzNMd\ncckNZNr/Fa6m2O4EUtzWa2kOPxb/e9tk4E8gFo8102KVu/ZqCoWtX5aWaDDxO1h77LHH8NJLL0Gj\n0QAAJk+ejPr6eskGRhSPxB0CzDBjPewtAvwt5ulul6Wj5Eb1ZwfRktICzLafG21FOz1lubyW5vBj\nzZm3YqUMxGKHxWLEPfd0o709EwYDMG7c91BZuRnp6YA4Sxuu3cZE0SCgRu7Z2dmCY4VCuuKDRPFI\nPMVjgMH5a3+LeXpqsL59+w40m4woq14ZtUU7g2rx48eaMxYrjQ8Gw0rIZHtx333244wMhXOjjDhL\n29CwiOvYaNDwO1hLS0vDuXPnIJPJAAAfffQR0u1/3SEiP4mneEbnq6HMSQ2omKe3XZa2DgAvAdAB\nUNuAbwMIQRUK1/V1jRPSkFWRF9T6uqBa/Pix5ozFSvvEcs24QHYQB3IuUazzO1j71a9+BY1Gg6+/\n/ho///nPUVdXh23btkk5NqK44pjiaW0bipMNHWhLT0HibYnImp0b0I5N8S7Lrq5j+OKLXCQkZGHT\npknYs2c/gL6ddKEocitult3VZQ5bIVIu/g9MebkSVW+1ArOXojbzFGqeVOPQvY9EbFdwINztIA7F\nuUSxzu9g7YorrsCf//xnfPqp/T+Ob37zm8jIyJBsYETxxjHFs26t/fhgoxkVx96AonplQFkh112W\nXV3HYLN1AgAsltP46qtGwbniaddgBdssOxS45iwwOp0cmL0UuPxFAIAeNSirjo0WXO52EIfiXKJY\n53ew1t7ejrS0NBQXF+PEiRN49913cf3113PdGpGfxNM0eb3Tk4Hu2HTdZfnFF7mC94YP78F//tN3\nrFYXBHRtR1N0+9ovtbNeWTibZdPAqNVW1GaeErwWzl3BA1n4724HcSjOJYp1fgdr//M//4O//vWv\nuHjxIn75y19iwoQJePfdd/HQQw9JOT6iuCGetjH0JosGsmMzISELFstp5/GvfpWDoUO/G3Qjdk/1\nylybZaf3rlmj6FRZaULNk2roUeN8LZy7gj1tgCGi4PkdrNlsNqSmpmLv3r34yU9+grvuuouN3YkC\n4Ji26TB9hc9bzuNtYxZKC8cPaOeiWr0XOt2N6OkxIiEhC1dcsRfbt4/1eL6vtlSe6pW5NsvOzk5H\nU1Nb0GMmaalUwKF7H0FZtTUiu2O58J8o9PwO1rq6utDd3Y333nsPt9xyCwBALmf/PCJ/uU7bTAQw\n75qBX1OpHIuJE48JXvO2G9BdKx/XDQje6pUFw2gxo9xQD113N9QKBSrzRkOVGFyXBvJfJHfHcuE/\nUej5HazdcMMNuPbaa6FWq/Gtb30LTU1NSE5OlnJsRBQEb0Vk3bXycRXqemXlhnpUtdp3kdaa7Gve\nto+SdhdpLJeuCBcpC8py4T9R6PmdGlu2bBnefvttvPjii5DL5UhNTcUf/vAH5/uHDx+WZIBEFBhv\nRWTVarXgPfEGBEdTdHVGgbMperPJGPxYRE0dxcdScASrtbUJqKpKQllZCArNxRnHujKT6VO0tu6G\nwbAyZNd2ZJALC9/BqFE72FWAKAQC6mAwdOhQ56/T0tKQlpbmPN6yZQumTp0aupERkVMgmRBvRWQd\nbam8bUDw1hQ9UGqFwplRcxxLzZ+OB4Md15URxZaAgjVvbDZbqC5FRCKB7LDzVkRWpcryWSTXW1P0\nQFXm2XeRuq5Zk5o/HQ8Gu0isK/O1uYWIPAtZsOZoQ0VEoRdIJmSgRWRDuclAlZgk+Ro1MXY88C0S\n68p8bW4hIs9CFqwRkXTCmQmJ9abo7HjgWyQKyvra3EJEnnEalCiC/J0aCmcmhE3RSQpqtbo3o+Y4\nLojcYIhiTMiCtRUrVoTqUkSDhr9TQ+JMCMtTUKzxZ3MLEbnnM1i7++67va5He/TRRwEAxcXFoRsV\nURTy1DdzIIKdGvJWS40oGvmzuYWI3PMZrE2fPj0c4yCKer5KWgRTaDTYqSGWp6Bw4S5OosjzGazN\nnTs3HOMginq+SloE08A62KkhZ3kK5Xlg9lLUjdFC89bokGT7KPSkyMqGC3dxEkWe32vWLBYLXn75\nZRw7dgxdXV3O1zdu3CjJwIiija+SFsEUGlWpsrDpsc3OP8jLPl7h1x/kjnIU1dlL0DLyH2gBUKX9\nBAMpYEv+CSbwCmWh4X7jkTjzxV2cRJHnd7C2bt069PT04KOPPsLPfvYz7NmzB1dffbWUYyOKKr5K\nWgRbXiOYP8gd5SlK/qFFbVPf6wMpYEv+CeZ5BVNo2GI0w1Bej25dNxRqBfIqR6NV2dYvUJQ688Vd\nnESR53ewduTIEbz++uu46aabcPvtt2PhwoVYunSplGMjiiq+SloEW15jIB0DQlnA1pXMaMSQ8pVI\n0NWhR61Ge+UW2LhOCUBwzyuY52Qor0drVQsAwFTbgZbuFvzceAv0DaeBTKB2tj0wkzrztXr1OtTU\nfIzmZiNUqiysWbMupNcnIt/8DtaSk5MBAAkJCejs7ER6ejrOnz8v2cCIYk2whUYHEnBJVcB2SPlK\nKHuzNUm92Zo2rlMCENzzCuY5deuETe83fPA76FtO2w/09n/p1HWSZ742bqyAXm+/b2fnaWzY8ADX\nrBGFmd/B2tChQ3HhwgVcd9110Gg0UKlUyM3NlXJsRFFDyrpmAwm4pCpgmyDKzoiPB7Ngnlcwz0mh\nVsBU2+E8NsAgPKHFHijaN6VIV7+Ma9aIIs/vYO2Pf/wjEhISsGLFCrz++utoa2vDnDlzpBwbUdSQ\nsq5ZNHYM6FGrezNqjuOCyA0myoTreeVV2pveO9asFXaPwxf7Pne+nz9qBCqLN9vHI2Gmi2vWiCLP\n72Dt9ddfxw9/+EMolUqUlpZKOSaiqDPY6pq195YUsa9ZK0A7q82HXaIqCaO2FzqPH2l+FHJFgiCD\nFo7yH+w8QBR5fgdrBw8exKZNmzBjxgzMmzcPV111lZTjIooqzrpmLsfxzKbK4ho1CQVT/iNSHQDY\neYAo8vwO1h577DG0tLRgz549+N3vfoeLFy9i3rx5uP3226UcH4F9IKOBo66Z6zMg8sRXMCZl3bVI\ncFdmJFGVFOlhEcWNgBq5Z2Zm4pZbbsHs2bOxefNmbN26lcFaGLAPZOQ56poR+cNXMDaQci3RSFxm\nBIBgCpeIBsbvhTc9PT04dOgQ7rrrLtxwww2w2Wz461//KuXYqNdgWy9FFOsEwVcHUL3lIEpKpkGj\nuRXNzUaoM9SC80NVHy9SxGVGxMdENDB+Z9aKi4sxYcIEzJkzBw8//DCUSqWU4yIXg229FFGsE9Ri\n2wO0fNGCWnzq7DBQ+Qdp6uNFirjMiEKtiOBoiOKP38HaP/7xD+Tl5Xl8/6WXXsKCBQtCMigS4nop\notjiWoutrvMkWtDifE+nq4vKci0DIS4z4jgmotDwO1jzFqgBwPPPP89gTSJcL0UUW1yDMc3+W1F1\narfzvXisUyYuM0JEoRXQBgNvbDZbqC5FRBQ3BlKnjLssiQgIYbAmk8lCdSmiQYflWeLXQOqUcZcl\nEQEhDNaIKHgsz0LucJclEQF+lO7o6OjwdQoAToMSDQTLs5A74l2V3GVJNDj5/BPhlltuAQCsWrXK\n63kPPfRQaEZENAiJy7GwPAsB9l2WGaWZUBalIqM0k7ssiQYpn9OgnZ2dOHr0KD7//HNotdp+GbRx\n48YBAL7xjW9IM0KiQYDlWeJDqNcecpclEQF+BGs///nPUVZWhvr6emg0GsF7MpkMBw4ckGxwRFIz\nGo0oL1/Zu1NPjcrKLVCpvDfUlgLLs4ROME3S/b3W6ivWYeO6Co+/X7j2kIik4DNYW7hwIRYuXIgV\nK1Zgy5YtQd3k8OHD2LBhA2w2G+bPn4/Fixe7Pe+tt97CPffcg5dffhmTJk0K6l5EgSgvX4mqqt4e\njr3V5YPduRdu3EHqXiibpIuvVfP4x9B/dNp+7Ob3S6TXHoYyUCWi6OH3btBgAzWr1YqKigrs2LED\nOTk5WLBgAWbOnInCQmFq/+LFi/jLX/6CoqKioO5DFAydrs7rcTRzZnHS/3979x4cZX33ffyThIQE\nOSU0UEDccGORUTNgiyilJRwkViOFAA62SlVqaIt4gJakarmdWwQkdMwt8ojiYdLiAZSCqcRDn4kK\njCAwjwQQ7GCUJAohRDYpIQSWJL/nD5KVzYFsNnvtXru8X39x7eHa7+aH5OPveE6Fkw9py2enlTI0\nWtn9r1B8l9Dbi+vC8Nn/vyqktPtVdqbjocOfh6Q3f29lmdPz+WZ/X4J9NJw/gyoA+/A6rH322Wda\nsWKFvvnmG9XX18sYo4iICO3YseOi79u3b58cDocGDhwoSUpLS1NBQUGLsPbMM88oIyNDL730kg9f\nA/CNw+Fo7CFpuk6SFBo9FO5em4cPSRMqVCUp7+T5h14cFJh5Tv7ctNVjCPHKh6RvfAsdHudyqnOH\npDe/V3z/BNWWHvn++WanEQR77qE/gyoA+/A6rD322GOaO3euRowYochI77v2y8vLPY6q6tevn/bv\n3+/xmoMHD+rYsWNKSUkhrKFd/pxn1tbu8sHooWg5PypHy/57QJvDnO5enP6egaDEFbi9uPy5aavH\nkGHvw57PdSB0XHguZ2cPSW9+r0fX/LeW/vcTbZ5GEOy5h/4MqgDsw+uwFhsbq8mTJ/u9AGOMli1b\npuXLl3s85o3ExB5+rweB42v7zZt3n8c8s65do7V+/Xqfa3j77X+0ePxo7Tctrq3++zbvrfs8AuL/\n+yxa3+ad/16FhVGN3/P717/yivSHP0j/92ysKlXtfnxoj8ssr7Xp/qVH6z0eN0frff7soUOlwsLG\ni6rB0uW7v38u8Uqv75uoHnr7rpZt6ovW7tXa3xe7eGX6S/rD5mgdrjqswb0Ha/Vtq5UQ1/Lnxr+d\noYu2uzR5HdbGjh2rLVu2KCUlpUMf0K9fPx09etR9XV5err59+7qva2pqVFRUpFmzZskYo++++05z\n587V6tWr211kUFFRfdHnYV+JiT18br9Dh4paXPv778KAuMsl7b7gepDlf98OVXh+rxMNX3s+f6he\nFRWem1SvWiVV1vVXZtk5fXXmtJxVn+vgtr9o6p6+lg3dXth2EQOiPJ6LGBDl889p8WLp7NnGOWsN\nK6VBdY1z1pK0+MZs/nv3SrRWjft+dKL+lFRxyvPn1pn/9hBctF1o60zQ9jqsrV+/Xi+88IIuu+wy\nxcTEeD1nLTk5WaWlpTpy5IgSExOVn5+vp5/+fuige/fuHveYNWuWHnnkEV199dU+fB1cCtqaZ+ZP\n/hxK81aL+VFKUu2Fz7cxWT2+S7ReHDREGR/co8+/2qijkj4vlwIxdNu0SeuFc9Z85TmE2E1Sbqfr\nA4Bw4HVY+8c/fOv6j4qK0qJFizR79mwZYzRjxgwNGTJEK1euVHJyssaPH+/x+oiICI6uwkW1Nc/M\nn+JjEwK+iq7F/Kjhf9XS4nNeT1YPxuRyNm0FAOtFmA4ko7q6Oh0+fH7i7+DBg9WlS3DPgac7OHTR\nne9/GR/crbyvNrmvpwyZZkngpO1CG+0Xumi70BaQYdD9+/frwQcfdA+B1tXV6dlnn2XzWsAmgjF0\nCwCwntdhbcmSJVq6dKlGjx4tSdqxY4cWL16sdevWWVZcKGN3eQRaMIZuAQDW83rDtNraWndQk6TR\no0ertrb2Iu+4tDVt8FlYGKW8vGhlZsYGuyTgkuSsdSrjg3uU+tY4ZXxwtyrPONt/EwDYiNc9a3Fx\ncdq5c6duuOEGSdKuXbsUFxdnWWGhLthnBAI4jyOYAIS6Dp1g0DRnTZLOnTunlStXWlZYqAv2GYEA\nzvPHKlmmNQAIJq/DWnV1tTZs2KATJ05Ikvr06aNDhw5ZVlioC/YZgQDO88cRTB7nljb+T1gwj5UC\ncGnxOqxlZ2dr06ZN6tOnjySpoaHB/RhaCvYZgQDO88cqWaY1AAgmr8Na04kFTSIjI1VfX3+RdwBA\n4DmdTmVlLWjcNNmh7OycTs9RY1oDgGDyOqxddtll2rt3r4YPHy5J2rt3r7p162ZZYQDgi6ysBcrL\na1xQUNi4oODF3E7dk2kNAILJ67C2cOFC3X///bryyislSUVFRVq1apVlhQFAW+qc51SWVepxJmmX\n+PNzykpKij1e2/zaF0xrABBMXoe16667Tvn5+SosLJQkjRgxQr169bKsMIQHZ61TWVsXNM4Xcig7\nJUfxsQnBLgsWqKtzqqxsgVyuYsXEONS/f466dLGmrcuySnUyr0qSdKbwtCS5zyh1OByNPWpqvE6y\npAYACJQOHe7Zq1cvpaSkWFULwlA47nFFAG1dWdkCnTx5vq3PnDnf1oMG5VryWa4SV5vX2dmNCwpK\niuVwJCk723/HbtH2AIIhuCexI+z5Y48ruwnHAOoPLlfxRa/9KcYR4+5Ra7puEh+f0Ok5am2h7QEE\nA2ENlvLHHld2E44B1B9iYhyNPWpN10mWfVb/7CskyWPOWiDQ9gCCgbAGS/ljjyu7CccA6g/9+59v\n6/Nz1pLUv791bd0lPto9Ry2QaHsAwUBYg6XiYxPCbpgoHAOoP3TpkmDZHDW7oO0BBEOEMcYEuwhf\nVVRUB7sE+CgxsYet2o+zH71nt7ZDx9B+oYu2C22JiT18fi89a4A4+xEAYF8ccAcofM5+dDqdysi4\nR6mp45SRcbcqK53BLgkA0En0rAEKn7MfrThqKZDaG45u7dzP+Hj2OQMQ3ghr8Fo4/6IMl7MfrThq\nKZDaG44O9TAKAL4grMFr4fyLMlzOfrT7UUvtBf72hqNDPYwCgC8Ia/Aavyjtz8qjlqTzxy3Ne+s+\nHaoo8um4pfYCf3vD0c3DaHHk18r44G6OfQIQ1ghr8Jrde21g7VFLUuePW2ov8Lc3HN0URrfs+1BV\ncVWquqlKeV9t6nAdABBKCGvwmtW9NrC/zh631DzwHz9erspKp3sotL3h6KYwmvrWOI+TBDj2CUA4\nC839CRAUTb8o//Wvj/Xii7lhs7gA3nP0dDS7TurQ+7OzczRgwED39dGjR5SZuSDgdQBAKKFnDYDX\nslNy1LVrdOOctY4ftxQfn6C+ffvp6NEj7sd8mfvIsU8ALiWENQBei49N0Prb13fqyJvW5j52dFuY\ncDxzFgDaQlgDEFCtzX3MzAzfbWEAoLMIawACqrUVq2wLAwBtY4EBgKBzOJotGGBbGABwo2cNgF85\na53K2rqgcfK/dxvnsi0MALSNsAZcAqw817X5vV03u/Te8XxJ3m+ca/VmvgAQyghrwCXAynNdm9+7\n9ze9pV9+/zwb1gJA5xDWgDDT2jCklRP4W9yr0vOSDWsBoHMIa0CYae38TivPdW1+79HX/EwxQ2LY\nsBYA/ISwBoSZ1s7vXJ+9Uf6YwO90OjVv3n06dKjIPfettcUBHEUGAP5DWAP8zFnr1PwP79eOsk8k\nSaMHjtH/jv8/7a6I9BdHT4fHIeeOnkl+m8Df1tw3FgcAgHUIa4CfZW1doPdK8t3X7x3OV0xk14Ad\nj2TluZlsXtu+Ouc5lWWVylXiUowjRv2zr1CX+OhglwUghBHWAD9rbfVjIFdEenNupq9beVg59y1c\nlGWV6mRelSTpTOFpSdKgF4cEsyQAIY6wBvhZ82HI848lBaeYRs17e/7H9T/653tvS+rYVh7Z2Tnq\n2jW6cc4am9e2xlXiuug1AHQUYQ0B56x1at5b9+lQRZHXO9yHkuyUHLnqXBfMWftZ0FdENu/tKer9\npcfz3g5nxscnaP369aqoqPZ3iWEjxhHj7lFrugaAziCsIeBa21oiUPO5AiE+NkF/S3sj2GV4aN67\n80P9UAd1wH3NcKb/9M++QpI85qwBQGcQ1hBwrW0tAWs17+35y+hF6hHTi7M4LdAlPpo5agD8irCG\ngGtta4lQY+VZm1Zorbfnxfjc4BYFAPAKYQ0Bl53SOEm9oihkd7i36qzN1o6K8sd8Pnp7ACB0EdYQ\ncPGxCVp/e2hPUrdqv7Fwn88HAOi4yGAXAIQih8PR7DrJL/dlPh8AoDl61gAftHYepj+Ew3w+AIB/\nEdYAH/jrrM3mslNy5Kp2acdLn0iVkuuas6oc5bT14gUAgLUYBrURp9OpjIx7lJo6ThkZd6uy0hns\nkuAHHWnX+NgExXzQVVWfVanqcJXe25yvzMwFAawWAGA39KzZiFUrDBFcHW1XDksHAFyInjUb4Zd0\nePK2XZt64IqLv/Z4nNMFAODSRlizEatWGMI3/hqW9rZdm3rgqqrOn+HZu3dvTZkyzevFCwyjA0B4\nYhjURqxaYQjf+GtY2tt2bd7jlpT0Xx36PIbRASA8EdZsxKoVhvCNv4alvW1Xh8PRGLKarpM69DkM\nowNAeCKsAW3obHjqqM72rAa6XgBAYBDWgDa0FZ6sOsS9sz2rDKMDQHiKMMaYYBfhq1A+W/JSl5jY\nI2TbLyPjHvfcMEmaMmXaJTV8HcptB9ovlNF2oS0xsYfP72U1KNBBzA0DAAQSYQ3oILZYAQAEEnPW\ngA5ibhgAIJAIa0AHscUKACCQGAYF/IDTAwAAVqFnrZOs2sYBoYXTAwAAViGsdRK/pCGxQhQAYB2G\nQTuJX9KXntaGPFkhCgCwCj1rncQRP8HjrHUqa+sClZwslqOnQ9kpOYqPtX4IurXeVFaIAgCsQljr\nJH5JB0/W1gXK+6oxNFU0DkHfnGv557bWm8oKUQCAVQIS1rZu3aqlS5fKGKPp06drzpw5Hs/n5ubq\nrbfeUpcuXZSQkKClS5eqf//+gSit0/glHTwlJ4svem0VelMBAIFkeVhraGjQ4sWLlZubq759+2rG\njBmaOHGihgwZ4n7N1VdfrY0bN6pr16564403lJ2drZycHKtLQ4hz9HQ09qg1XScF5HPpTQUABJLl\nYW3fvn1yOBwaOHCgJCktLU0FBQUeYW3UqFHuP48YMULvvPOO1WUhDGSnNIamk8Vy9ExSdkpgQhO9\nqQCAQLI8rJWXl3sMafbr10/79+9v8/UbNmzQ2LFjrS4r5LCfW0vxsQkBmaMGAEAw2WqBQV5eng4c\nOKC1a9d69frExB4WV2Qf8+bd57ECsWvXaK1fvz7IVXXOpdR+7Tlx4oTmzp2rw4cPa/DgwVq9erUS\nEuwbxmm70Eb7hS7a7tJkeVjr16+fjh496r4uLy9X3759W7xu+/btWrNmjV599VVFR0d7de+Kimq/\n1Wl3hw4VtbgO5e+fmNgjpOv3t4yMOe4wvnv3bp09W2fboVbaLrTRfqGLtgttnQnalm+Km5ycrNLS\nUh05ckQul0v5+fmaOHGix2sOHjyoxx9/XKtXr1Z8fLzVJYUkNl0Nb2yuDABoi+U9a1FRUVq0aJFm\nz54tY4xmzJihIUOGaOXKlUpOTtb48eO1YsUK1dbW6qGHHpIxRgMGDNBzzz1ndWkhhRWI4Y3tQAAA\nbYkwxphgF+EruoNDF935niorncrMXOARxu26gIS2C220X+ii7UJbZ4ZBbbXAAGhPuK6KZTsQAEBb\nCGsIKa2dy0nIAQCEM8sXGAD+5MtEfKfTqYyMe5SaOk4ZGXerstJpTXEAAFiAnjWEFF8m4tMbBwAI\nZYQ1hBRfVsWyLQYAIJQR1hBSfJmIz7YYAIBQRlhD2GOPOgBAKCOsIeyxLQYAIJSxGhQAAMDGCGsA\nAAA2RlgDAACwMcIaAACAjRHWAAAAbIywBgAAYGOENQAAABsjrAEAANgYYQ0AAMDGCGsAAAA2RlgD\nAACwMcIaAACAjRHWAAAAbIyw1gqn06mMjHuUmjpOGRl3q7LSGeySAADAJapLsAuwo6ysBcrL2yhJ\nKiz8TFKEXnwxN6g1AQCASxM9a60oKSm+6DUAAECgENZa4XA4ml0nBacQAABwyWMYtBXZ2TmSIlRS\nUiyHI0nZ2U93+p5Op1NZWQsa7+lQdnaO4uMTOl8sAAAIa4S1VsTHJ/h9jhrz4AAAgC8YBg0Q5sEB\nAABfENYChHlwAADAFwyDBogV8+AAAED4I6wFiBXz4AAAQPhjGBQAAMDGCGsAAAA2RlgDAACwMcIa\nAACAjRHWAAAAbIywBgAAYGOENQAAABsjrAEAANgYYQ0AAMDGCGsAAAA2RlgDAACwMcIaAACAjRHW\nAAAAbIywBgAAYGOENQAAABsjrAEAANgYYQ0AAMDGCGsAAAA2RlgDAACwMcIaAACAjRHWAAAAbIyw\nBgAAYGOENQAAABsjrAEAANgYYQ0AAMDGCGsAAAA2RlgDAACwMcIaAACAjRHWAAAAbIywBgAAdxAo\nkgAADiBJREFUYGOENQAAABsjrAEAANhYyIa1UaNGKSPjblVWOoNdCgAAgGW6BLsAX+3evVvSbkkR\nevHF3CBXAwAAYI2Q7VlrUlJSHOwSAAAALBPyYc3hSAp2CQAAAJYJ2WHQ66+/XgMGDFJ29tPBLgUA\nAMAyIRvWdu3apYqK6mCX4RWn06msrAUqKSmWw+FQdnaOjFGLx+LjE4JdKgAAsJmAhLWtW7dq6dKl\nMsZo+vTpmjNnjsfzLpdLWVlZOnDggOLj45WTk6MBAwYEorSAyMpaoLy8jZKkwsLPJEVIUovHWCgB\nAACas3zOWkNDgxYvXqyXX35ZmzdvVn5+vr766iuP12zYsEG9evXSv/71L919991asWKF1WUFVPNF\nECUlxa0+BgAA0JzlYW3fvn1yOBwaOHCgoqOjlZaWpoKCAo/XFBQUKD09XZJ08803a8eOHVaXFVAO\nh6PZdVKrjwEAADRn+TBoeXm5+vfv777u16+f9u/f7/Ga48eP64c//KEkKSoqSj179lRVVZV69+5t\ndXkBkZ2dIymicX5a0gWLIlp7DAAA4Hu2XGBgjPHqdYmJPSyuxD8SE3vo7bf/0eLx1h67lIRK+6El\n2i600X6hi7a7NFk+DNqvXz8dPXrUfV1eXq6+ffu2eM2xY8ckSfX19Tp16lTY9KoBAAB0huVhLTk5\nWaWlpTpy5IhcLpfy8/M1ceJEj9eMHz9emzZtkiS9//77uvHGG60uCwAAICREGG/HHDth69atWrJk\niYwxmjFjhubMmaOVK1cqOTlZ48ePl8vl0sKFC/XFF1+od+/eevrpp3X55ZdbXRYAAIDtBSSsAQAA\nwDchfzYoAABAOCOsAQAA2BhhDQAAwMZsH9a2bt2qX/ziF7r55pu1Zs2aFs+7XC7Nnz9fqampmjlz\npsc2IQiu9touNzdXaWlpmjJliu69916VlZUFoUq0pb32a/LBBx9o2LBhOnDgQACrw8V403bvvvuu\n0tLSNHnyZP3pT38KcIW4mPbar6ysTL/5zW+Unp6uKVOmaMuWLUGoEq159NFH9dOf/lSTJ09u8zVP\nPvmkUlNTNWXKFH3xxRfe3djYWH19vbnpppvMt99+a1wul/nlL39pioqKPF7z2muvmccff9wYY0x+\nfr55+OGHg1ApmvOm7Xbu3GnOnDljjDHm9ddfp+1sxJv2M8aYU6dOmTvvvNPMnDnTfP7550GoFM15\n03bFxcUmPT3dVFdXG2OMOXHiRDBKRSu8ab9FixaZN954wxhjTFFRkRk/fnwwSkUrdu/ebQ4ePGhu\nu+22Vp//+OOPTUZGhjHGmMLCQnP77bd7dV9b96xxrmjo8qbtRo0apa5du0qSRowYofLy8mCUilZ4\n036S9MwzzygjI0PR0dFBqBKt8abt3nzzTf36179W9+7dJUkJCQnBKBWt8Kb9IiIidOrUKUnSyZMn\n1a9fv2CUilaMHDlSPXv2bPP5goICTZ06VZI0fPhwVVdX67vvvmv3vrYOa62dK3r8+HGP17R1riiC\ny5u2u9CGDRs0duzYQJQGL3jTfgcPHtSxY8eUkpIS6PJwEd60XXFxsQ4fPqxf/epXuuOOO7Rt27ZA\nl4k2eNN+8+bNU15enlJSUvT73/9eixYtCnSZ8NGFmUU6377edFTY8mzQzjBsGxdy8vLydODAAa1d\nuzbYpcBLxhgtW7ZMy5cv93gMoaG+vl6lpaV67bXXdPToUd11113avHmzu6cN9pafn6/p06frnnvu\nUWFhoRYuXKj8/PxglwUL2bpnjXNFQ5c3bSdJ27dv15o1a7R69WqG0mykvfarqalRUVGRZs2apQkT\nJmjv3r2aO3cuiwxswNt/NydMmKDIyEhdfvnlSkpKUnFxcYArRWu8ab8NGzbolltukXR+CsnZs2fl\ndDoDWid807dvX3dmkaRjx455NYxt67DGuaKhy5u2O3jwoB5//HGtXr1a8fHxQaoUrWmv/bp3764d\nO3aooKBAH374oYYPH67nn39e11xzTRCrhuTdf3s33XSTdu7cKUlyOp0qKSnRoEGDglEumvGm/QYM\nGKDt27dLkr766iu5XC7mHdrIxUYZJk6cqLfffluSVFhYqJ49e+oHP/hBu/e09TBoVFSUFi1apNmz\nZ7vPFR0yZIjHuaK33367Fi5cqNTUVPe5ogg+b9puxYoVqq2t1UMPPSRjjAYMGKDnnnsu2KVD3rXf\nhSIiIhgGtQlv2u7nP/+5PvnkE6WlpSkqKkqZmZnq1atXsEuHvGu/rKws/eUvf1Fubq4iIyM9piMg\nuP74xz9q586dqqqq0rhx4/TAAw/o3LlzioiI0MyZM5WSkqItW7Zo0qRJiouL07Jly7y6L2eDAgAA\n2Jith0EBAAAudYQ1AAAAGyOsAQAA2BhhDQAAwMYIawAAADZGWAMAALAxwhoAAICNEdYA2Mbrr7+u\nW265RdOmTdPp06f9dt9NmzbpwQcf9Nv9OiM9PV0ul0uStGfPHk2ePFnTpk3Tzp079bvf/U7ffPPN\nRd+/cuVKvffee5KkXbt26ZNPPrG8ZgDBZesTDABcWl599VWtWLFC1157rd/vHRER4fd7+qLpeDxJ\nysvLU3p6umbPni1JuuGGG9p9/4Whc9euXaqpqdGYMWP8XygA2yCsAeiUPXv2aMWKFaqpqVFERIQy\nMzPVo0cPLVmyRLW1tYqLi9Njjz2m5ORkHTlyRNOnT9fMmTO1detWnTlzRkuWLNGPf/xjzZ8/X6Wl\npcrMzNQ111yjFStWtPissrIy3X777dqyZYuioqIknQ8vEyZM0OTJkzVnzhz95z//0dmzZ5WcnKwn\nnnhCXbp498/cqlWr9O6776pr166KiIjQ3//+d3Xv3l3Dhg3T/fffr4KCAp09e1bz589XamqqJGnf\nvn3661//qpqaGnctKSkpkqSPPvpIq1atUl1dnaKiovTUU09p6NChGjZsmPbs2aPXX39d7733nmJj\nY/XOO+9o3bp1uuWWW7RmzRpdeeWVKi8v15IlS1RcXKyIiAilpaVpzpw5euSRR3Tttdfq+uuv17p1\n62SM0aeffqpbb71VZWVlGjhwoH77299KOn/+7oIFC/T+++93up0BBJEBAB9VVVWZMWPGmMLCQmOM\nMQ0NDea7774z48aNM59++qkxxpjt27ebcePGmXPnzplvv/3WXHXVVebjjz82xhjzz3/+09xxxx3u\n+40fP94UFRVd9DPvvfde8+GHHxpjjKmsrDQ33nijqa2tddfTJDMz06xbt84YY8zGjRvNgw8+eNHv\nMXLkSHP27FljjDE1NTWmvr7eGGPMVVddZZ577jljjDFff/21GTVqlDlx4oQ5efKkmTp1qqmoqDDG\nGHP8+HEzduxYU11dbb7++mszZswYU1paaowxxuVymZqaGmOMMcOGDTOnT582xhjz5z//2bz66qse\n3//LL780xhgza9Ys88orr7ifq6ysbPGeZ5991ixfvtz9mqKiIjNp0iT39aOPPmrWrl170Z8nAPuj\nZw2AzwoLC3XllVdq+PDhks4PNZ44cUIxMTHuIb3Ro0crJiZGhw8fVrdu3XTZZZe5e59GjBjR4hBq\n085xxVOnTtXGjRs1fvx4bd68WRMmTFBsbKwaGhr00ksvadu2baqvr1d1dbXi4uK8+h49evSQw+FQ\nZmamxowZo3Hjxqlbt27u52fMmCFJGjx4sK699lrt3btXkZGR+vbbb5WRkeGuOSoqSiUlJSosLFRK\nSooGDRokSYqOjlZ0dLRX30+STp8+rT179uhvf/ub+7HevXu3+74hQ4Zo0KBB2rZtm4YPH66PPvpI\njzzyiFc/AwD2RVgDYLkLA0pMTIz7z5GRkaqvr+/QvVJTU/XUU0+pqqpKGzdu1GOPPSZJeuedd7Rn\nzx698cYbiouL0wsvvKDi4mKv7hkZGak333xTn332mXbs2KFp06bp5Zdf1tChQ1vUf+Gfhw0bprVr\n17a4X2FhYZuf5e3cuYiICBljOjzXbtasWXrttddUVFSkSZMmqXv37h16PwD7YTUoAJ+NGDFCRUVF\n2rt3rySpoaFBffr00blz57Rr1y5J0o4dO1RXV6fBgwdLatmz5E1P04ViY2M1ceJEPf3006qpqdFP\nfvITSVJ1dbXi4+MVFxen6upqbd682et71tTU6MSJExo5cqQeeOABDR06VF9++aX7+Y0bN0qSiouL\n9cUXX2j48OG67rrrVFxcrJ07d7pft3//fknSz372M23ZskWlpaWSJJfL5V7d6s337datm6677jrl\n5ua6H6usrGzxuu7du+vUqVMej6WkpOjw4cPKzc3VnXfe6eVPAICd0bMGwGe9evXSqlWrtGzZMp0+\nfVpRUVHKzMzUypUr9eSTT7oXGDz77LPuif7Ne4ouvPa2F2nq1Km666679PDDD3s8VlBQoFtvvVV9\n+vTRyJEjdebMGa/ud+rUKT3wwAM6e/asGhoadM0112jSpEnu5+vq6pSenq4zZ85o8eLFSkhIkCSt\nXr1ay5cv17Jly+RyuXTFFVfo+eefl8Ph0JNPPqmHH35Y9fX1ioqK0vLly/WjH/3oot/xwueys7P1\nxBNPaNOmTYqKitJtt92m++67z+P1N910k+bNm6f09HTdeuutysjIUEREhNLT07Vt2zZ3zyCA0BZh\nOvq/tQBwCRk2bJgKCwsVGxsb7FK8Nnv2bN1xxx3uVasAQhvDoABwEU1zx0LB559/rkmTJqlnz54E\nNSCM0LMGwHb+/e9/689//rN7WLBpov2dd97pXpnpiy1btignJ6fFfefPn6+xY8f6pXYA8DfCGgAA\ngI0xDAoAAGBjhDUAAAAbI6wBAADYGGENAADAxv4/e1fwBGQIvcAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60f82c4450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FMerJcVT86iC2ozbizh1K8s9Swv7OsWOziI6O5uDBA3zjG1PD/j4Rkf7It3NA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60f2711b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "it = itertools.combinations(weights.keys(),2)\n",
    "plt.figure(figsize=(10,7))\n",
    "for each in it:\n",
    "    for dataset in dfTrain.dataset.unique():\n",
    "        plt.scatter(x=dfVal['conf_'+each[0]][dfVal.dataset == dataset],\n",
    "                    y=dfVal['conf_'+each[1]][dfVal.dataset == dataset],\n",
    "                    color=colors[dataset],\n",
    "                    label=dataset,\n",
    "                    marker=\"o\",\n",
    "#                     linewidth=\n",
    "                   )\n",
    "#     plt.scatter(x=dfTrain['conf_'+each[0]], y=dfTrain['conf_'+each[1]], color=[colors[dataset] for dataset in dfTrain['dataset']],marker=\"*\")\n",
    "    plt.xlabel('conf_'+each[0])\n",
    "    plt.xlim(0,1)\n",
    "    plt.ylim(0,1)\n",
    "    plt.ylabel('conf_'+each[1])\n",
    "    plt.legend(loc=2,fontsize='large')\n",
    "    plt.show()\n",
    "    plt.figure(figsize=(10,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G/QVRbWXJgO8VGZPQ5zF3Q8lfA6lTu8OFQ6ctOHDKgnaHgLhoHW5ckIb0lL7d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\nzdPIQTZrdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60f8da0b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=dfTrain['conf_val_specificity'], y=dfTrain['conf_val_sensitivity'], kind=\"kde\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize confidence distributions accross recordings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of recordings 63\n"
     ]
    },
    {
     "data": {
      "image/png": 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44zBgVsjElMvl0llnnaU9e/Zo586d/n/NkZubq54964aude/eXXl5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\n/LiohAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60fd201c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_0008.wav\n",
      "train_0010.wav\n",
      "train_0028.wav\n",
      "train_0040.wav\n",
      "train_0052.wav\n",
      "train_0061.wav\n",
      "train_0071.wav\n",
      "train_0083.wav\n",
      "train_0093.wav\n",
      "train_0095.wav\n",
      "train_0097.wav\n",
      "train_0101.wav\n",
      "train_0104.wav\n",
      "train_0105.wav\n",
      "train_0106.wav\n",
      "train_0128.wav\n",
      "train_0133.wav\n",
      "train_0134.wav\n",
      "train_0136.wav\n",
      "train_0152.wav\n",
      "train_0158.wav\n",
      "train_0161.wav\n",
      "train_0167.wav\n",
      "train_0172.wav\n",
      "train_0175.wav\n",
      "train_0177.wav\n",
      "train_0192.wav\n",
      "train_0197.wav\n",
      "train_0209.wav\n",
      "train_0211.wav\n",
      "train_0212.wav\n",
      "train_0233.wav\n",
      "train_0238.wav\n",
      "train_0239.wav\n",
      "train_0249.wav\n",
      "train_0254.wav\n",
      "train_0282.wav\n",
      "train_0291.wav\n",
      "train_0292.wav\n",
      "train_0294.wav\n",
      "train_0298.wav\n",
      "train_0299.wav\n",
      "train_0301.wav\n",
      "train_0311.wav\n",
      "train_0313.wav\n",
      "train_0317.wav\n",
      "train_0332.wav\n",
      "train_0344.wav\n",
      "train_0357.wav\n",
      "train_0363.wav\n",
      "train_0385.wav\n",
      "train_0390.wav\n",
      "train_0431.wav\n",
      "train_0432.wav\n",
      "train_0448.wav\n",
      "train_0458.wav\n",
      "train_0463.wav\n",
      "train_0466.wav\n",
      "train_0468.wav\n",
      "train_0475.wav\n",
      "train_0484.wav\n",
      "train_0493.wav\n",
      "train_0500.wav\n"
     ]
    }
   ],
   "source": [
    "test = dfTrain[dfTrain.conf_mean < .4][dfTrain.conf_var < .002][dfTrain.dataset == 'x']\n",
    "print(\"Number of recordings %d\" % (test.count()[0]))\n",
    "test.set_index(\"filenames\",inplace=True)\n",
    "test = test.transpose()\n",
    "test = test.iloc[6:-2]\n",
    "test.set_index(np.r_[0:test.count()[0]],inplace=True)\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.violinplot(data=test,jitter=True)\n",
    "plt.ylabel(\"Confidence for true class\")\n",
    "plt.title(\"Recordings which are confusing during training\")\n",
    "plt.ylim(0,1)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "for names in test.columns.values:\n",
    "    print(\"%s\" % names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170\n",
      "24\n",
      "17\n",
      "83\n",
      "17\n",
      "1\n",
      "12\n",
      "0\n",
      "109\n",
      "1699\n",
      "3\n",
      "55\n",
      "381\n",
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "for dataset in dfTrain.dataset.unique():\n",
    "    for true in dfTrain.true.unique():\n",
    "        test = dfTrain[dfTrain.conf_mean > .55][dfTrain.true == true][dfTrain.dataset == dataset].count()[0]\n",
    "        print(\"%d\" % (test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation set inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15511/15511 [==============================] - 2s 98us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 97us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
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      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 2s 98us/step\n",
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      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 2s 98us/step\n",
      "15511/15511 [==============================] - 2s 103us/step\n",
      "15511/15511 [==============================] - 2s 101us/step\n",
      "15511/15511 [==============================] - 1s 93us/step\n",
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      "15511/15511 [==============================] - 1s 94us/step\n",
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      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 93us/step\n",
      "15511/15511 [==============================] - 1s 93us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 93us/step\n",
      "15511/15511 [==============================] - 1s 93us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 93us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 1s 96us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 95us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n",
      "15511/15511 [==============================] - 1s 94us/step\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(min_epoch,df.epoch.count()):\n",
    "    checkpoint_name = os.path.join(model_dir+log_name,\n",
    "                                   \"weights.%.4d-%.4f.hdf5\" % (epoch+1,df.val_acc.iloc[epoch]))\n",
    "    model.load_weights(checkpoint_name)\n",
    "    y_pred = model.predict(x_val, verbose=1)\n",
    "    y_pred_hard = np.argmax(y_pred,axis=-1)\n",
    "    predicted_confidence = np.asarray([y_pred[i,j] for i,j in enumerate(y_val_cc)])\n",
    "    start_idx=0\n",
    "    pred = []\n",
    "    for s in val_parts:\n",
    "        if not s:  ## for e00032 in validation0 there was no cardiac cycle\n",
    "            continue\n",
    "        # ~ print \"part {} start {} stop {}\".format(s,star_idx,start_idx+int(s)-1)\n",
    "        temp = sum(predicted_confidence[start_idx:start_idx + int(s) - 1])/s\n",
    "        pred.append(temp)\n",
    "        start_idx = start_idx + int(s)\n",
    "    dfVal[\"weights.%.4d-%.4f.hdf5\" % (epoch+1,df.val_acc.iloc[epoch])]=pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xBJYsWYp16z7DK6+8UavG0aPHYOfO7fjqq/U4cOAXLFz4oFsDFMAQRURERI04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\nCCGEEA6QIkoIIYQQwgH/BWwxuUkZ5PDzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60f2450610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dfVal['conf_mean'] = dfVal[list(dfVal.columns.values)[-100:]].mean(axis=1).values\n",
    "# dfVal['conf_var'] = dfVal[list(dfVal.columns.values)[-100:]].var(axis=1).values\n",
    "plt.figure(figsize=(10,7))\n",
    "metric = 'val_sensitivity'\n",
    "for dataset in dfTrain.dataset.unique():\n",
    "#     sns.distplot(dfVal['conf_'+metric][dfVal.true == 1],bins=20,kde_kws={'bw':.04},label='Abnormal')\n",
    "    if dataset != 'x':\n",
    "        continue\n",
    "    sns.distplot(dfVal['conf_'+metric][dfVal.true == 1][dfVal.dataset == dataset],bins=20,kde_kws={'bw':.04},label='dataset '+dataset)\n",
    "    sns.distplot(dfVal['conf_'+metric][dfVal.true == 0][dfVal.dataset == dataset],bins=20,kde_kws={'bw':.04},label='dataset '+dataset)\n",
    "plt.legend()\n",
    "plt.xlim([0,1])\n",
    "plt.xlabel(\"Confidence for true class\")\n",
    "plt.title(\"Validation Recording Confidence for Normal PCG (Best \"+metric+\" Weights)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfTrain.to_csv('/media/taufiq/Data1/heart_sound/dfTrain.csv',index=None)\n",
    "dfVal.to_csv('/media/taufiq/Data1/heart_sound/dfVal.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfTrain = pd.read_csv('/media/taufiq/Data1/heart_sound/dfTrain.csv')\n",
    "dfVal = pd.read_csv('/media/taufiq/Data1/heart_sound/dfVal.csv')"
   ]
  }
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